Speaker
Description
The Bayesian approach in general has a lot to offer in times of Machine Learning (ML) and Artificial Intelligence (AI). The Bayesian framework itself offers a learning environment, where the prior, and, subsequently the posterior distributions can be updated sequentially, and where human expertise can be incorporated. The approach allows for uncertainty quantification of all quantities of interest. Moreover, it safeguards against overfitting. These advantages reveal a promising near future for the Bayesian approach in times of ML and AI.
The approach is computationally challenging and requires not only statistical and programming skills, but also the awareness of the decisions made in the data analysis process. Here, the workflow of applied Bayesian statistics helps with iterative model building, model validation and comparison,
and with diagnosing computational problems.
The choice of the prior distributions is a critical part of the Bayesian analysis. In the talk, we will briefly discuss suitable prior choices for important learning problems like Bayesian variable selection in high dimensions and Bayesian neural nets.
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