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🎙️ Speakers: Thomas Wiecki, Niall Oulton, Tim McWilliams, Carlos Trujillo, Kemble Fletcher, Evan Wimpey \| ⏰ Time: 16:00 UTC / 9:00 AM PT / 12:00 PM ET / 5:00 PM Berlin

Marketing measurement is evolving faster than most teams can keep up, and 2025 pushed every model, method, and assumption to its limits. With shifting budgets, new privacy pressures, and a market full of hype disguised as innovation, the real question is: what actually worked?

In this session, the PyMC Labs team opens the curtain on what we learned from working hands-on with some of the world’s leading brands, across MMM, CLV, forecasting, causal inference, generative AI, and fully custom Bayesian models. Instead of polished slides or scripted talking points, this roundtable is a guided, honest conversation about what this year revealed, and what 2026 will demand from marketing leaders.

Drawing from dozens of real client engagements, model builds, and experiments, you’ll see how our team approached this year’s hardest measurement problems, where the industry is heading, and how to think more clearly about marketing effectiveness in a chaotic environment.

You’ll learn:

• The biggest challenges brands faced in MMM, CLV, forecasting, and attribution in 2025.
• The breakthroughs that actually moved the needle, and the ones that didn’t.
• What turned out to be genuine measurement trends vs. overhyped noise.
• How Bayesian modeling helped solve problems traditional approaches couldn’t.
• The solutions, playbooks, and ideas our team used to drive meaningful decisions.
• What 2026 will require from marketing measurement, experimentation, and strategy.

Join us for a sharp, candid, and practitioner-led discussion that surfaces the lessons, surprises, and strategies shaping smarter marketing decisions, not theory, but what we’ve seen in the trenches.

📜 Outline of Talk / Agenda:

• 5 min: Introduction to PyMC Labs and speakers
• 40 min: Panel discussion
• 15 min: Q&A

💼 About the speakers: Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience. 🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

Niall Oulton (Vice President of Sales - PyMC Labs) Niall Oulton has built a reputation as a leading expert in the field of marketing analytics, with a specialization in Bayesian Marketing Mix Modelling. His career, spanning over a decade, has seen him on both sides of the business landscape - agency and client. His rich background provides him with a unique perspective, making him an expert in understanding and navigating the complexities of both worlds.

🔗 Connect with Niall: 👉 LinkedIn: https://www.linkedin.com/in/nialloulton20/ 👉 Twitter: https://twitter.com/niall20 👉 GitHub: https://github.com/nialloulton 👉 Website: https://1749.io/

Tim McWilliams (Principal Data Scientist - PyMC Labs) With over 7 years of experience in the marketing mix modeling and marketing analytics space, Tim specializes in applying Bayesian modeling techniques to solve complex business challenges and uncover actionable insights. Passionate about bridging advanced statistical methods with real-world marketing strategy, he has worked across diverse industries to optimize media investments and measure impact.

🔗 Connect with Tim: 👉 LinkedIn: https://www.linkedin.com/in/tim-mcwilliams-a4b647b3/ 👉 Github: https://github.com/timbo112711

Kemble Fletcher (Director of Product Development - PyMC Labs) Before joining PyMC Labs, Kemble co-founded SweepLift and co-invented its patent-pending in-stream survey and measurement technology. He later led omnichannel attribution and measurement strategy at Google for its top 300 global clients, influencing $2B in ARR. Prior to that, he drove digital analytics and predictive modeling at OMD for brands like Levi’s, Hilton, and eHarmony. He also advises SaaS and start-up leaders on data architecture, attribution, and growth. At PyMC Labs, Kemble helps organizations solve complex challenges through advanced Bayesian modeling.

🔗 Connect with Kemble: 👉 LinkedIn: https://www.linkedin.com/in/kemblefletcher/

Carlos Trujillo (Principal Data Scientist - PyMC Labs) Carlos is a Marketing Scientist passionate about using data and AI to turn marketing strategy into measurable results. He’s worked with teams across Latin America, Europe, and Africa, including roles at Wise, Bolt, and Omnicom Media Group. As a core member of PyMC Labs, he contributes to open-source projects like PyMC-Marketing, blending statistical rigor with practical marketing insight.

🔗 Connect with Carlos: 👉 LinkedIn: https://www.linkedin.com/in/cetagostini/ 👉 Github: https://github.com/cetagostini

💼 About the Host: Evan Wimpey (Director of Analytics at PyMC Labs) Evan helps clients design Bayesian solutions tailored to their goals, ensuring they understand both the how and why of inference. With master’s degrees in Economics and Analytics, he focuses on delivering clear value throughout projects and brings a unique twist with his background in data comedy. 🔗 Connect with Evan: 👉 Linkedin: https://www.linkedin.com/in/evan-wimpey/ 👉 GitHub: https://github.com/ewimpey

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/ 🎮 Discord: https://discord.gg/mTc64cAz

In this session, the PyMC Labs team explores how Large Language Models (LLMs) and their Semantic Similarity Rating (SSR) methodology can replicate human purchase intent with up to 90% of human test-retest reliability. Building on this research, we’ll introduce the Synthetic Consumers Platform—a breakthrough in simulated customer insights that generates reliable, human-like survey data at scale and in a fraction of the time. You’ll learn how synthetic consumers and SSR can transform customer research by enabling faster iteration and significantly reducing costs, and how an AI-driven insights platform accelerates testing, validation, and decision-making in marketing research and creative testing.

Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI. Investing for Programmers shows you how you can turn your existing skills as a programmer into a knack for making sharper investment choices. You’ll learn how to use the Python ecosystem, modern analytic methods, and cutting-edge AI tools to make better decisions and improve the odds of long-term financial success. In Investing for Programmers you’ll learn how to: Build stock analysis tools and predictive models Identify market-beating investment opportunities Design and evaluate algorithmic trading strategies Use AI to automate investment research Analyze market sentiments with media data mining In Investing for Programmers you'll learn the basics of financial investment as you conduct real market analysis, connect with trading APIs to automate buy-sell, and develop a systematic approach to risk management. Don’t worry—there’s no dodgy financial advice or flimsy get-rich-quick schemes. Real-life examples help you build your own intuition about financial markets, and make better decisions for retirement, financial independence, and getting more from your hard-earned money. About the Technology A programmer has a unique edge when it comes to investing. Using open-source Python libraries and AI tools, you can perform sophisticated analysis normally reserved for expensive financial professionals. This book guides you step-by-step through building your own stock analysis tools, forecasting models, and more so you can make smart, data-driven investment decisions. About the Book Investing for Programmers shows you how to analyze investment opportunities using Python and machine learning. In this easy-to-read handbook, experienced algorithmic investor Stefan Papp shows you how to use Pandas, NumPy, and Matplotlib to dissect stock market data, uncover patterns, and build your own trading models. You’ll also discover how to use AI agents and LLMs to enhance your financial research and decision-making process. What's Inside Build stock analysis tools and predictive models Design algorithmic trading strategies Use AI to automate investment research Analyze market sentiment with media data mining About the Reader For professional and hobbyist Python programmers with basic personal finance experience. About the Author Stefan Papp combines 20 years of investment experience in stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and software consultant. Quotes Especially valuable for anyone looking to improve their investing. - Armen Kherlopian, Covenant Venture Capital A great breadth of topics—from basic finance concepts to cutting-edge technology. - Ilya Kipnis, Quantstrat Trader A top tip for people who want to leverage development skills to improve their investment possibilities. - Michael Zambiasi, Raiffeisen Digital Bank Brilliantly bridges the worlds of coding and finance. - Thomas Wiecki, PyMC Labs

🎙️ Speaker: Andy Heusser\, Luca Fiaschi \| ⏰ Time: 4 PM UTC / 9 AM PT / 12 PM ET / 6 PM Berlin

Insight Agents are purpose‑built AI coworkers that transform demanding analytical workflows into push‑button tasks. Built on a modular blend of retrieval‑augmented generation (RAG), tool calling, and sandboxed code execution, each agent automates the full statistical pipeline—from data exploration and validation to model fitting and interpretation—without requiring deep technical expertise.

The session showcases our Marketing Mix Modeling (MMM) Insight Agent, which compresses weeks of Bayesian MMM work into minutes by delegating tasks to specialized sub‑agents. You’ll see how this architecture delivers secure, explainable, and scalable results that let marketers focus on strategy instead of code.

MMM is only the first stop. We plan to extend the same framework to prototype Insight Agents for customer life-time value, causal impact analysis and more. We’ll dig into the design principles, share implementation lessons, and outline the roadmap from today’s collaborative “copilots” to tomorrow’s autonomous digital coworkers that proactively surface insights and drive better business outcomes.

Read More:

• https://www.pymc-labs.com/blog-posts/the-ai-mmm-agent/
• https://www.linkedin.com/feed/update/urn:li:activity:7299850936309444608/

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Andy Heusser, PhD (Principal Data Scientist at PyMC Labs) Andy is a data science leader with over 15 years of experience. He began his career in academia, leveraging computational models and brain imaging to study human memory, as well as developing AI-driven tutoring algorithms using language models. He then served as the Director of Data Science at a digital health startup, where he led teams in building machine learning and AI solutions for digital mental health products. Today, he runs a data science consulting company called Cognitive Insight Consulting and is a Principal Data Scientist at PyMC Labs.

🔗 Connect with Andy: 👉 Linkedin: https://www.linkedin.com/in/andrew-heusser-3b6587b1/ 👉Github: https://github.com/andrewheusser

1. Luca Fiaschi, PhD - Partner at PyMC Labs With over 15 years of leadership experience in AI, data science, and analytics, Luca has driven transformative growth in technology-first businesses, such as Chief Data & AI Officer at Mistplay (+$200M) where he led revenue growth through AI-powered personalization and dynamic pricing algorithm, and holding executive roles at global industry leaders such as HelloFresh ($8B), Stitch Fix ($1.2B), and Rocket Internet ($1B). His core competencies span machine learning, artificial intelligence, analytics, data engineering, and computer vision, which he has applied for consumer-facing products within CPG, gaming, e-commerce, food delivery, meal kits, and healthcare, working with both fast-paced startups and F100 companies. Luca is a partner at PyMC Labs, providing insights and guidance on Generative AI to Fortune 500 companies. He holds a PhD in AI and Computer Vision from Heidelberg University and has more than 450 citations on his research work.

🔗 Connect with Luca: 👉 Linkedin: https://www.linkedin.com/in/lfiaschi/

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience. 🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 Website: https://www.pymc-labs.com/, https://twiecki.io/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: William Dean\, Corey Abshire\, Thomas Wiecki \| ⏰ Time: 5 PM UTC / 9 AM PT / 12 PM ET / 6 PM Berlin

In this event, we will discuss how customers can use Databricks to develop and productionize MMM models for their companies. By combining Databricks capabilities in consolidating, organizing and securing data pipelines and manage ML models and pipelines with PyMC-Marketing’s easy to use modelling capabilities, companies can bring develop sophisticated MMM models to help understand, optimize and forecast their marketing budgets.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Corey Abshire, Senior AI Specialist Solutions Architect, DataBricks Corey Abshire is a Senior Specialist Solutions Architect focused on GenAI and ML in Communications, Media and Entertainment. Prior to Databricks, Corey was a Principal Machine Learning Engineer at Cummins Inc., working on critical data science and machine learning initiatives for quality, engineering, and manufacturing. He holds an M.S. in data science from Indiana University in Bloomington, IN.

🔗 Connect with Corey: 👉 Linkedin: https://www.linkedin.com/in/coreyabshire/

1. Will Dean, Principal Data Scientist, PyMC Labs Will Dean is a Statistician and Data Scientist with experience in geospatial and user analytics. He is passionate about Bayesian methods and using data visualization to tell a story. He is interested in software design and how it can make data problems easier and more enjoyable to solve.

🔗 Connect with Will Dean: 👉 Linkedin: https://www.linkedin.com/in/williambdean/ 👉 Github: https://github.com/wd60622/

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience. 🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 Website: https://www.pymc-labs.com/, https://twiecki.io/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Aleksander Molak\, Thomas Wiecki\, Carlos Trujillo \| ⏰ Time: 14:00 UTC / 7:00 AM PT / 10:00 AM ET / 4:00 PM Berlin

Have you ever wondered about the difference between Bayesian and Causal Modeling? Or how these two approaches can help improve your data analysis? This event is for you!

Join us for an open conversation with our experts, where we’ll explore the key differences, best use cases, and practical tips for using both Bayesian and Causal methods.

What You’ll Learn:

• What makes Bayesian and Causal Modeling different and when to use each.
• Real-life examples and advice from experienced professionals.
• A chance to ask questions and be part of the discussion.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Aleksander Molak (Author of "Causal Inference & Discovery in Python") Alex is on a mission to make complex ideas simple and easy to understand. He’s an independent machine learning researcher, author, and educator, specializing in causality, NLP, and AI strategy.

🔗 Connect with Alex: 👉 Website: https://alxndr.io/ 👉 Youtube: https://www.youtube.com/@CausalPython 👉 Linkedin: https://www.linkedin.com/in/aleksandermolak 👉 Github: https://github.com/alxndrmlk

1. Carlos Trujillo Agostini (Data Science at PyMC Labs)

Carlos, a seasoned marketing scientist at PyMC Labs, has built a career advancing Marketing Mix Modeling through structured causal models, transforming how data is used in marketing strategies.

🔗 Connect with Carlos: 👉 GitHub: https://github.com/cetagostini 👉 LinkedIn: https://linkedin.com/in/cetagostini

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Jesse Grabowski \| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

Time series are everywhere, and building time into our models can bring them to the next level. Modeling time series, however, can be a minefield. They can be especially hard in PyMC, where one needs to have deep knowledge of both pytensor and PyMC internals to set up recursive models, handle missing data, make out of sample forecasts, or generate IRFs. This talk aims to introduce PyMC users to the pymc-statespace module, a collection of tools designed to help users past these hurdles and into time series modeling. The talk will introduce the linear gaussian state space framework, and give end-to-end bayesian time series workflow examples. This will include pre-defined models like SARIMAX, modular structural time series models built from latent components, and an example of implementing a complex custom model, using a Dynamic Stochastic General Equilibrium (DSGE) as a motivating example.

Resources

Example notebooks in pymc-experimental:

• SARMA
• VARMA
• Structural Modeling
• Making a custom statespace model

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Jesse Grabowski PhD Candidate, Paris 1 Pantheon Sorbonne Principal Data Scientist, PyMC Labs Jesse Grabowski is an economist and data scientist, specializing in macroeconomics, finance, and machine learning. He has worked as a economic modeler at the Organization for Economic Cooperation and Development (OECD) Development Centre for several years. He currently works as a data scientist at PyMC labs, where he helps deliver elite-level solutions to complex business problems. He is currently a PhD candidate at Paris 1 Pantheon-Sorbonne, where his work focuses on quantifying the macroeconomic risks of tail climate risks using Bayesian methods.

🔗 Connect with Jesse: 👉 Linkedin: https://www.linkedin.com/in/jessegrabowski/ 👉 Github: https://github.com/jessegrabowski 👉 Youtube: https://www.youtube.com/@Thayme 👉 Website: http://jbgrabowski.com/

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Allen B. Downey \| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

If you commute by subway, you might have noticed that you can use the number of waiting passengers to predict the time until the next train. If there are fewer passengers than usual, you just missed a train and might have to wait longer. If there are more than usual, it's been a while since the last train, and you expect one soon. But if there are many more than usual, there might be a disruption of service and a long wait!

In this case study, we'll use PyMC to model this scenario. Starting simple, we'll demonstrate a process for developing and testing models incrementally, present some less commonly used PyMC features, and show how a Bayesian model can replicate Bayesian thinking.

Resources

We will assume that webinar participants are familiar with basic PyMC models and distributions like Normal, Poisson, and Gamma.

If you are not familiar with PyMC, you can start with this chapter from Think Bayes, especially the World Cup Problem: https://allendowney.github.io/ThinkBayes2/chap19.html

Or you can run that chapter on Colab https://colab.research.google.com/github/AllenDowney/ThinkBayes2/blob/master/notebooks/chap19_v3.ipynb

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Allen B. Downey Allen Downey is a Principal Data Scientist at PyMC Labs, professor emeritus at Olin College and the author of several books -- including Think Python, Think Bayes, and Probably Overthinking It -- and a blog about programming and data science. He received a Ph.D. in computer science from the University of California, Berkeley, and Bachelor's and Masters degrees from MIT.

🔗 Connect with Allen B. Downey: 👉 Linkedin: https://www.linkedin.com/in/allendowney/ 👉 Blog: https://www.allendowney.com/blog/ 👉 X: https://twitter.com/AllenDowney

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Dr. Ulf Aslak Lai\| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

Marketing Mix Modeling is a mathematical approach to estimating the effect of marketing on sales. It has become increasingly popular in recent years due to the increasing difficulty of traditional cookie-tracking for marketing attribution. In opposition to tracking-based methods, MMM models the effect of marketing using aggregated data like daily or weekly impressions and sales.

In this session, Dr. Ulf Aslak Lai joins Dr. Thomas Wiecki for a conversation about open-source software for Marketing Mix Modeling (MMM). Ulf will share some background on how he was introduced to the problem and eventually made his way into the community of PyMC-Marketing developers, then present a comprehensive overview of PyMC-Marketing focusing on recent innovations like experiment integration and time-varying coefficients. Thomas and Ulf will discuss the advantage of a Bayesian approach to MMM, and the challenges inherent to the modeling problem, and share some predictions for the future.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Dr. Ulf Aslak Lai Dr. Ulf Aslak Lai is a data scientist, and core PyMC-Marketing developer. With a Ph.D. in Complex Systems and extensive experience in both academia and industry, Ulf has over 10 years of experience developing advanced mathematical models for various industries. During his Ph.D. he published multiple high-impact research articles, including one in Nature. For the past two years he has been specializing in Bayesian marketing mix modeling and now dedicates most of his time to open-source and project work within this field.

🔗 Connect with Dr. Ulf Aslak Lai: 👉 Linkedin: https://www.linkedin.com/in/ulfaslak/ 👉 GitHub: https://github.com/ulfaslak/ 👉 X: https://twitter.com/ulfaslak

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Alexander Bor\| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

Understanding one’s customers is crucial and brings numerous benefits to any business, including increased customer satisfaction and retention as well as efficient sales and marketing strategies. However, such understanding is not always available, and an alternative approach must be sought. One such approach is Customer Lifetime Value (CLV) modeling which provides insight into customer behavior and allows to optimization of related business processes such as sales and marketing. This talk discusses CLV modeling and its application within the marine industry sector.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Alexander Bor Dr. Alexander Bor serves as a Senior Data Scientist within the Machine Learning and Advanced Analytics team at Wärtsilä. His work primarily revolves around field services, sales, and marketing projects. Prior to this role, Dr. Bor was solving optimization problems in the petrochemical and oil refining industries, as well as addressing workforce optimization challenges in the retail sector. He obtained his doctoral degree from Aalto University, where he specialized in the field of process control.

🔗 Connect with Alexander : 👉 LinkedIn: https://www.linkedin.com/in/alexbor1/

1. Colt Allen Colt Allen is the lead developer for open source customer lifetime value modeling. He is a Senior Data Scientist & Machine Learning Engineer in the fast casual restaurant industry, and previously worked in the energy sector. He is an INFORMS Certified Analytics Professional (CAP) and holds a masters degree in Mineral & Energy Economics from Colorado School of Mines.

🔗 Connect with Colt: 👉 LinkedIn: https://www.linkedin.com/in/coltallen-datascientist/

💼 About the Host:

1. Thomas Wiecki (CEO & Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Corey Neskey \| ⏰ Time: 17:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

Risk assessment is challenging when data is unavailable, hard to obtain, or costly to process. Organizations often request estimates from experts instead. This talk demonstrates how to integrate cybersecurity data with expert estimates using Bayesian modeling in PyMC. Cybersecurity analysts, resource managers, and executives can use Bayesian models to perform risk assessments, select security controls, and prioritize which suspicious events to investigate first. System administrators can configure autonomous sources of data including vulnerability scanners and cybersecurity event monitoring systems to automatically update these hybrid network models alongside inputs from risk analysts and executives.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Corey Neskey (Hive Systems)

Corey is Vice President of quantitative risk at Hive Systems, where he develops Derive, a powerful risk modeling and cybersecurity inference platform. His work combines expert knowledge elicitation methods with PyMC and Bayesian network models to solve problems in cybersecurity risk quantification and inform executive decision-making. Corey holds a MSc in cybersecurity intelligence and forensics, a CISSP, and undergraduate degrees in science and philosophy. Prior to joining Hive Systems, he worked at RSA, EMC, Dell SecureWorks, Bloomberg L.P, and NYU.

🔗 Connect with Corey: 👉 LinkedIn: https://www.linkedin.com/in/cneskey 👉 Website: https://www.deriverisk.com 👉 GitHub: https://github.com/cneskey 👉 Blog posts: https://www.hivesystems.io/blog?author=61400746eab56326f6a95503

💼 About the Host:

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 Website: https://www.pymc-labs.com/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Blog posts: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Niall Oulton\, Carlos Eduardo Trujillo Agostini \| ⏰ Time: 17:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

As the year draws to a close, it's time to reflect on the remarkable journey of PyMC-Marketing. We've witnessed significant advancements with the addition of several new features, and now, our users are beginning to harness their potential.

Join us on this special occasion, for an immersive experience. We're excited to present a live demo, meticulously designed to showcase a selection of compelling use cases. These cases are not just theoretical constructs; they are real-world applications demonstrated by actual users of PyMC-Marketing. Witness firsthand how our platform has been instrumental in their success.

To add more substance to our session and ensure it's not just informative but also engaging, we've planned a unique segment featuring the new GPT model developed by Niall. The new GPT will be the most powerful assistant to create MMM models.

Don't miss this opportunity to delve into the world of PyMC-Marketing, discover new use cases, and explore the ease of using GPT for your MMM models. We promise you an event filled with learning, interaction, and innovation.

Looking forward to an insightful and invigorating session with you!

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Niall Oulton

Niall Oulton has built a reputation as a leading expert in the field of marketing analytics, with a specialization in Bayesian Marketing Mix Modelling. His career, spanning over a decade, has seen him on both sides of the business landscape - agency and client. His rich background provides him with a unique perspective, making him an expert in understanding and navigating the complexities of both worlds.

Previously, Niall played an integral role in the development and deployment of an entire Bayesian MMM workflow at a global agency. This experience enabled him to gain valuable insight into the potential risks, benefits and pitfalls of in-housing a marketing effectiveness measurement programme.

🔗 Connect with Niall: 👉 LinkedIn: https://www.linkedin.com/in/nialloulton20/ 👉 Twitter: https://twitter.com/niall20 👉 GitHub: https://github.com/nialloulton 👉 Website: https://1749.io/

1. Carlos Eduardo Trujillo Agostini

Carlos, a seasoned marketing scientist with international experience, has built a career pushing the boundaries of traditional marketing strategies. Now, at Bolt, he's leading the evolution of their Marketing Mix Modeling through the innovative use of PyMC, demonstrating the transformative power of data-driven decision making in the marketing realm

🔗 Connect with Carlos: 👉 GitHub: https://github.com/cetagostini 👉 Linkedin: https://linkedin.com/in/cetagostini

💼 About the Host:

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 Website: https://www.pymc-labs.com/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Blog posts: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Matthew Johnston\, Pavel Knorr\, Tomas Capretto \| ⏰ Time: 17:30 UTC / 9:30 am PT / 12:30 pm ET / 6:30 pm Berlin

Bayesian Hierarchical Modeling has been a staple in the world of discrete choice models for decades. During this time, the scientific community has generated countless research papers and developed numerous tools and libraries (with around 99% of them written in R) to tackle common market research questions by constructing classical models using HBayes and MCMC.

However, in today's fast-paced commercial landscape dominated by cloud-based solutions, startups, and SaaS, many of these traditional solutions and libraries are falling short. Users often encounter challenges related to technical integration, licensing restrictions, and more. In this webinar, we'll take you through our journey in building a Bayesian SaaS product. Shedding light on the pain points we've experienced: data collection and encoding, model results interpretation, and performance issues. We will try to explain why, in this evolving landscape, PyMC emerges as the singular, user-friendly solution that bridges the gap between complex Bayesian modeling and practical usability.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Matthew Johnston (Founder and CEO at the EPIC Conjoint)

Matt Johnston is a seasoned commercial marketing professional with over 20 years of pricing, product and segmentation experience. Matt has an extensive background in telecommunications as former Head of Pricing at Telefónica Ireland and Ooredoo Group.

🔗 Connect with Matthew: 👉 LinkedIn: https://www.linkedin.com/in/matt-johnston-5a53672 👉 Website: https://www.epicconjoint.com/

1. Pavel Knorr (CTO at the EPIC Conjoint)

Pavel is a software developer and architect with more than ten years of professional experience and a strong foundation in machine learning and math. Currently, he holds the position of Chief Technical Officer at EPICConjoint, a startup specializing in market research and conjoint analysis. He is obtaining his Ph.D. in applied math.

🔗 Connect with Pavel Knorr: 👉 LinkedIn: https://www.linkedin.com/in/pavel-knorr/ 👉 Website: https://www.epicconjoint.com/

1. Tomas Capretto (Principal Data Scientist at PyMC Labs)

Tomi is a Principal Data Scientist at PyMC Labs, part-time PhD student, and statistics instructor at Universidad Nacional de Rosario. With a dedicated commitment to open-source development, Tomi's focus lies in the Bambi project.

🔗 Connect with Tomi: 👉 Website: https://tomicapretto.github.io/ 👉 GitHub: https://github.com/tomicapretto 👉 LinkedIn: https://www.linkedin.com/in/tom%C3%A1s-capretto-a89873106/

💼 About the Host:

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 Website: https://www.pymc-labs.com/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Blog posts: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

🎙️ Speaker: Robert Ness \| ⏰ Time: 17:00 UTC / 9am PT / 12pm ET / 6pm Berlin

Graphical causal inference and probabilistic programming share much history. For example, directed probabilistic graphical models were early versions of causal models and d-separation (graphical criteria for conditional independence) provided fundamentals for the do-calculus. Also, directed graphical models drove advancements in Bayesian inference algorithms and were the precursors of probabilistic programming languages like PyTorch. Further, both causal models and probabilistic programming favor explicitly modeling the data generating process. Yet, despite these commonalities, graphical causal inference and probabilistic programming have evolved into separate communities with little cross-talk beyond Bayesian inference of parameters in causal estimators. In this seminar, we discuss how to do causal graphical modeling with probabilistic programming, as well as tools and design patterns for doing so.

📑 Resources

• Causal AI Book: https://www.manning.com/books/causal-ai
• Related Paper: https://arxiv.org/abs/2102.06626
• Probabilistic Machine Learning Workshop: https://www.altdeep.ai/p/probml
• Causal Modeling in Machine Learning Workshop: https://www.altdeep.ai/p/causalml

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Robert Ness

Researcher at Microsoft Research, where he focuses on causal reasoning, deep probabilistic modeling, language models and programming languages. He is author of the book Causal AI, and founder of AI learning platform Altdeep.ai. He has worked as a research engineer and received his Ph.D. in statistics from Purdue University. He is a Johns Hopkins SAIS alumnus.

🔗 Connect with Robert Ness: 👉 LinkedIn: https://www.linkedin.com/in/osazuwa/ 👉 Twitter: https://twitter.com/osazuwa 👉 GitHub: https://github.com/altdeep/causalML 👉 MSR: https://www.microsoft.com/en-us/research/people/robertness/

💼 About the Host:

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 Website: https://www.pymc-labs.com/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Blog posts: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

🎙️ Speaker: Will Dean\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

Discuss using a classical Natural Language Processing technique for modeling weekly calendar data through a shift in vocabulary (pun intended). By using Latent Dirichlet Allocation to model discretized calendar events, the model’s probabilistic origin and Bayesian connection can be leveraged for various applications and insights.

For a sneak peek, check out the "latent-calendar" on PyPI or see an example here: https://wd60622.github.io/latent-calendar/examples/datasets/bikes-in-chicago/

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Will Dean Senior Data Scientist at FREENOW Will Dean is a Statistician and Data Scientist with experience in geospatial and user analytics. He is passionate about Bayesian methods and using data visualization to tell a story. He is interested in software design and how it can make data problems easier and more enjoyable to solve.

🔗 Connect with Will Dean: 👉 LinkedIn: https://www.linkedin.com/in/williambdean/ 👉 GitHub: https://github.com/wd60622

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 Website: https://www.pymc-labs.com/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

🎙️ Speaker: Thomas Wiecki \| ⏰ Time: 1 pm UTC / 6 am PT / 9 am ET / 3 pm Berlin

Causal analysis is rapidly gaining popularity, but why? Machine learning methods might help us predict what's going to happen with great accuracy, but what's the value of that if it doesn't tell us what to do to achieve a desirable outcome? Without a causal understanding of the world, it's often impossible to identify which actions lead to a desired outcome.

Causal analysis is often embedded in a frequentist framework, which comes with some well-documented baggage. In this talk, Thomas will present how we can super-charge PyMC for Bayesian Causal Analysis by using a powerful new feature: the do operator.

Content: 🎥 Async Recorded Talk: https://youtu.be/b47wmTdcICE 📖 Slides: Bayesian Causal Inference 📝 Code: Causal analysis with PyMC: Answering "What If?" with the new do operator 👉 Discourse Post for more details and discussion: https://discourse.pymc.io/t/12912

📢 Note: This session is exclusively for Q&A. We kindly request you to watch the recording before joining the event. For additional information and further discussion, please refer to this Discourse post.

💼 About the Speaker:

• Thomas Wiecki Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University, studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 Website: www.pymc-labs.com 👉 Twitter: https://twitter.com/twiecki 👉 GitHub: https://github.com/twiecki

🤝 Sponsor We thank our sponsors for supporting PyMC and the PyMCon Web Series. If you would like to sponsor us, contact us for more information.

Adia Lab is an independent, Abu Dhabi-based laboratory dedicated to basic and applied research in data and computational sciences. ADIA Lab focuses on societally-important topics such as climate change and energy transition, blockchain technology, financial inclusion and investing, decision making, automation, cybersecurity, health sciences, education, telecommunications, and space, by conducting cutting-edge research in Data Science, Artificial Intelligence, Machine Learning, and High-Performance Computing.

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🌐 Connecting with PyMC 📺 PyMCon Web Series: https://pymcon.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc/ 🐦 Twitter: https://twitter.com/pymc_devs 🎥 YouTube: https://www.youtube.com/@pymc-devs 🤝 Meetup: https://www.meetup.com/pymc-online-meetup/ 🐘 Mastodon: https://bayes.club/@pymc 💬 Discourse, Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc

🎙️ Speaker: Chris Fonnesbeck\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

Decision-making in sports has become increasingly data-driven with GPS, cameras, and other sensors providing streams of information at high spatial and temporal resolution. While machine learning is a popular approach for turning these data streams into actionable information, Bayesian statistical methods offer a robust alternative. They allow for the combining of multiple data sources, a natural means for imputing missing data, as well as full accounting for various system uncertainties.

In particular, hierarchical models provide a means for integrating information at multiple scales and adjusting for biases associated with small sample sizes. I will demonstrate a Bayesian workflow for model development using PyMC version 5, from data preparation through to the summarization of estimates and predictions, using baseball data.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Chris Fonnesbeck

Chris is the Principal Quantitative Analyst in Baseball Research & Development for the Philadelphia Phillies. He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.​

🔗 Connect with Chris: 👉 LinkedIn: https://www.linkedin.com/in/christopher-fonnesbeck-374a492a/

1. Dr. Thomas Wiecki (PyMC Labs)

Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

🎙️ Speaker: Carlos Agostini\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

Welcome to 'Mapping the Marketing Mix Journey: Bolt's Evolution towards MMM with PyMC.' This webinar is a deep dive into Bolt's unique path refining Marketing Mix Modeling (MMM). Guided by Carlos, a seasoned marketing scientist at Bolt, and co-hosted by Thomas Wiecki, author of PyMC, we'll take you through the various approaches Bolt initially considered for its MMM before ultimately deciding on PyMC.

In this session, Carlos will share insights into Bolt's methodology evolution, explaining why PyMC emerged as the tool of choice for their custom MMM. We'll discuss the advantages and challenges encountered during the transition and how these experiences have informed the company's current strategies.

The webinar will include a presentation session, an engaging debate on the use of PyMC, and an interactive Q&A segment to ensure we address your thoughts and queries.

Whether you're a marketer looking to enhance your MMM strategy or just curious about data science applications in marketing, this journey is certain to provide valuable insights. So, buckle up and join us as we navigate the exciting terrain of marketing science at Bolt

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Carlos Agostini Carlos, a seasoned marketing scientist with international experience, has built a career pushing the boundaries of traditional marketing strategies. Now, at Bolt, he's leading the evolution of their Marketing Mix Modeling through the innovative use of PyMC, demonstrating the transformative power of data-driven decision making in the marketing realm

🔗 Connect with Carlos Agostini: 👉 LinkedIn: https://linkedin.com/in/cetagostini 👉 GitHub: https://github.com/cetagostini

1. Dr. Thomas Wiecki (PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

🎙️ Speaker: Daniel Lee\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

This will be a high-level talk discussing the separation of statistical models and inference algorithms.

Things we’d like to talk about:

• The general vernacular combines two concepts together: model + inference. But they can be thought of separately.
• Given a statistical model, there are (at least) 3 different types of inference. Optimization, approximate inference, Bayesian inference. We’ll talk about some of the use cases of each. And where stochastic optimization fits in.
• A description of GPTs and how it can be implemented in Stan (and similarly in PyMC or any other PPL).

This talk won’t be overly technical. The goal will be to try to solidify the differences between the different types of inference and when to apply them. There will be plenty of time for Q&A.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Daniel Lee Daniel Lee is at Zelus Analytics working on player projection models across multiple sports. Daniel is a computational Bayesian statistician who helped create and develop Stan, the open-source statistical modeling language with over 20 years of experience in numeric computation and software; over 10 years of experience creating and working with Stan; and 5 years working on pharma-related models including joint models for estimating oncology treatment efficacy and PK/PD models. Past projects have covered estimating vote share for state and national elections; clinical trials for rare diseases and non-small-cell lung cancer; satellite control software for television and government; retail price sensitivity; data fusion for U.S. Navy applications; sabermetrics for an MLB team; and assessing “clutch” moments in NFL footage. He holds a B.S. in Mathematics with Computer Science from MIT, and a Master of Advanced Studies in Statistics from Cambridge University.

🔗 Connect with Daniel Lee: 👉 LinkedIn: https://www.linkedin.com/in/syclik/ 👉 Twitter: https://twitter.com/djsyclik 👉 GitHub: https://github.com/syclik 👉 Website: https://syclik.com/ 👉 Blog: https://medium.com/@bayesianops

1. Dr. Thomas Wiecki (PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/

🎙️ Speaker: Niall Oulton\, Thomas Wiecki \| ⏰ Time: 16:00 UTC / 9am PT / 12pm ET / 6pm Berlin

Join us for a dynamic discussion on building your own in-house marketing analytics solution and mastering marketing effectiveness:

In today's marketing landscape, understanding how your marketing efforts impact your business has become a critical component for success. This meetup will provide an exploration of marketing effectiveness measurement and why it's integral to long-term growth..

Join our expert panel as they navigate the pros and cons of outsourcing marketing analytics to an agency versus developing an in-house solution. The discussion will encompass agency benefits like broad industry expertise, advanced tools, and unbiased perspectives, weighed against the financial commitments, reduced autonomy, and transparency issues. On the other hand, in-house solutions offer control over data and processes, domain knowledge, responsiveness, confidentiality, and potential cost savings, but come with their own challenges.

We are excited to introduce a potential solution - PyMC - Marketing (https://www.pymc-marketing.io/en/stable/index.html). This Bayesian, open-source software offers a transparent, modifiable solution. It's an exciting advancement in the marketing analytics space, perfect to build an in-house marketing measurement solution, as seen by Bolt in their latest case study: https://bolt.eu/en/blog/budgeting-with-bayesian-models-pymc-marketing/

Don't miss this opportunity to gain insights from industry leaders and meet like-minded professionals who are passionate about leveraging data for marketing effectiveness. Whether you're a seasoned CMO or a marketing analyst, there's something for everyone in this meetup.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Niall Oulton Niall Oulton has built a reputation as a leading expert in the field of marketing analytics, with a specialization in Bayesian Marketing Mix Modelling. His career, spanning over a decade, has seen him on both sides of the business landscape - agency and client. His rich background provides him with a unique perspective, making him an expert in understanding and navigating the complexities of both worlds. Previously, Niall played an integral role in the development and deployment of an entire Bayesian MMM workflow at a global agency. This experience enabled him to gain valuable insight into the potential risks, benefits and pitfalls of in-housing a marketing effectiveness measurement programme.

🔗 Connect with Niall Oulton: 👉 LinkedIn: https://www.linkedin.com/in/nialloulton20/ 👉 Twitter: https://twitter.com/niall20 👉 GitHub: https://github.com/nialloulton 👉 Website: https://1749.io/

1. Dr. Thomas Wiecki (PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki: 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🔗 Connecting with PyMC Open Source: 💬 Q&A/Discussion: https://discourse.pymc.io 🐙 GitHub: https://github.com/pymc-devs/pymc 💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany 🐥 Twitter: https://twitter.com/pymc_devs 📺 YouTube: https://www.youtube.com/c/PyMCDevelopers 🎉 Meetup: https://www.meetup.com/pymc-online-meetup/