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Title & Speakers	Event
Model Evaluation and Discrete Choice Scenarios 2024-03-27 · 18:00 Abstract: Predictive Models are inadequate decision making tools in product demand analysis. Accept no substitutes for bayesian discrete choice models and causal inference. We will cover some of the history of discrete choice modelling and how to implement hierarchical variations of these models in PyMC. Along the way we'll derive some considerations about model adequacy and principles of model evaluation. Speaker: Nathaniel Forde Nathaniel is a data scientist specialising in probabilistic modelling for the study of risk and causal inference. He has experience in model development, deployment, multivariate testing and monitoring. He is broadly interested in questions of inference and measurement in the face of natural variation and confounding. Nathaniel's academic background is in mathematical logic and philosophy. Follow the Bayesian Mixer on LinkedIn to stay up to date.	Model Evaluation and Discrete Choice Scenarios

Abstract: Predictive Models are inadequate decision making tools in product demand analysis. Accept no substitutes for bayesian discrete choice models and causal inference. We will cover some of the history of discrete choice modelling and how to implement hierarchical variations of these models in PyMC. Along the way we'll derive some considerations about model adequacy and principles of model evaluation.

Speaker: Nathaniel Forde

Nathaniel is a data scientist specialising in probabilistic modelling for the study of risk and causal inference. He has experience in model development, deployment, multivariate testing and monitoring. He is broadly interested in questions of inference and measurement in the face of natural variation and confounding. Nathaniel's academic background is in mathematical logic and philosophy.

Follow the Bayesian Mixer on LinkedIn to stay up to date.

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