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[Online] State of Bayes Lecture Series #7 Gaussian Processes for Time Series 2023-07-06 · 16:00 🎙️ Speaker: Maxim Kochurov \\| ⏰ Time: 9am PT / 12pm ET / 6pm Berlin State of Bayes is a series of webinars about advances in practical methods and modeling intuition. The major focus of the webinar series will be on understanding concepts of advanced statistical models and introducing prior knowledge into the loop. This free course will be interesting for Bayesian practitioners who want to deepen their understanding about Bayesian modeling. In the closing webinar session we'll meet Gaussian processes for time series analysis. There are some very enlightening applications that bring GP to the number one most useful models in practice: Most known are seasonality decompositions for time series. Less known but very useful is time varying parameter for a model (an example of that you can see here: https://www.pymc-labs.io/blog-posts/modelling-changes-marketing-effectiveness-over-time/) During the webinar we'll get familiar with necessary concepts to apply a GP on a time series. At the coding session we'll revisit Rolling regression example from pymc-examples and will make that even more cool than ever before. Subscribe to our PyMCLabs YouTube to see past videos. Feedback Form: https://forms.gle/xHsaVzWrzDxRjhrt5 Sessions (generally bi-weekly) The full course is: Session 1️⃣: Introduction ▶️ L1: VIDEO Session 2️⃣: Bayesian Thinking ▶️ L2: VIDEO Session 3️⃣: Hierarchical modeling ▶️ L3: VIDEO Session 4️⃣: Interpretable Linear Regressions ▶️ L4: VIDEO Session 5️⃣: Bayesian AB testing ▶️ L5: VIDEO Session 6️⃣: Gaussian Processes ▶️ L6:VIDEO Session 7️⃣: Gaussian Processes for Time Series (6th July) 💼 About the speaker: Maxim Kochurov Maxim is a core developer of PyMC, a probabilistic programming language. Since the foundation of PyMC Labs he helps to improve complex statistical models and create a reusable solution. Besides strong expertise in Bayesian modeling his background includes economics, software engineering, and large-scale computer vision. 🔗 Connect with Maxim: 👉 LinkedIn: https://www.linkedin.com/in/ferrine 👉 Twitter: https://twitter.com/ferrine96 👉 GitHub: https://github.com/ferrine 👉 Website: https://ferrine.github.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/	[Online] State of Bayes Lecture Series #7 Gaussian Processes for Time Series

🎙️ Speaker: Maxim Kochurov \| ⏰ Time: 9am PT / 12pm ET / 6pm Berlin

State of Bayes is a series of webinars about advances in practical methods and modeling intuition. The major focus of the webinar series will be on understanding concepts of advanced statistical models and introducing prior knowledge into the loop. This free course will be interesting for Bayesian practitioners who want to deepen their understanding about Bayesian modeling.

In the closing webinar session we'll meet Gaussian processes for time series analysis. There are some very enlightening applications that bring GP to the number one most useful models in practice:

Most known are seasonality decompositions for time series.
Less known but very useful is time varying parameter for a model (an example of that you can see here: https://www.pymc-labs.io/blog-posts/modelling-changes-marketing-effectiveness-over-time/)

During the webinar we'll get familiar with necessary concepts to apply a GP on a time series. At the coding session we'll revisit Rolling regression example from pymc-examples and will make that even more cool than ever before.

Subscribe to our PyMCLabs YouTube to see past videos.
Feedback Form: https://forms.gle/xHsaVzWrzDxRjhrt5

Sessions (generally bi-weekly) The full course is:

Session 1️⃣: Introduction ▶️ L1: VIDEO
Session 2️⃣: Bayesian Thinking ▶️ L2: VIDEO
Session 3️⃣: Hierarchical modeling ▶️ L3: VIDEO
Session 4️⃣: Interpretable Linear Regressions ▶️ L4: VIDEO
Session 5️⃣: Bayesian AB testing ▶️ L5: VIDEO
Session 6️⃣: Gaussian Processes ▶️ L6:VIDEO
Session 7️⃣: Gaussian Processes for Time Series (6th July)

💼 About the speaker:

Maxim Kochurov Maxim is a core developer of PyMC, a probabilistic programming language. Since the foundation of PyMC Labs he helps to improve complex statistical models and create a reusable solution. Besides strong expertise in Bayesian modeling his background includes economics, software engineering, and large-scale computer vision.

🔗 Connect with Maxim: 👉 LinkedIn: https://www.linkedin.com/in/ferrine 👉 Twitter: https://twitter.com/ferrine96 👉 GitHub: https://github.com/ferrine 👉 Website: https://ferrine.github.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/

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