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The Benefits of Bayesian Machine Learning

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If you have a question about this talk, please contact Catherine Pearson.

Bayesian predictive models (a.k.a model-based machine learning or probabilistic programming) have several advantages over classic machine learning methods, including the ability to incorporate and propagate all sources of uncertainty and to include external information. In addition, predictions and model outputs are easy to interpret and there are many ways to check the model and understand where it goes wrong. Surprisingly, cross-validation to tune hyperparameters is not required because hyperparameters can be learned from the data. The benefits of Bayesian ML will be discussed with examples from preclinical research in the pharmaceutical industry.

This talk is part of the Institute for Energy and Environmental Flows (IEEF) series.

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