Machine Learning Coffee Seminar: "Probabilistic programming: Bayesian modeling made easy"

Probabilistic programming: Bayesian modeling made easy

 

Arto Klami

Academy Research Fellow, University of Helsinki

 

Abstract:

Probabilistic models are principled tools for understanding data, but difficulty of inference limits the complexity of models we can actually use. Often we need to develop specific inference algorithms for new models (which might take months), and need to restrict ourselves to tractable model families that might not match our beliefs about the data. Probabilistic programming promises to fix this, by separating the model description from the inference: With probabilistic programming languages we can specify complex models using a high-level programming language, letting a black-box inference engine take care of the tricky details. This talk covers the basic idea of probabilistic programming and discusses how well its promises hold now and in the future.

 

The next talks are:

23.1. at 9:15 in Otaniemi CS building T5: Juho Rousu "Metabolite identification through machine learning"

30.1. at 9:15 in Kumpula Exactum D123: Jukka Corander "Likelihood-free inference and predictions for computational epidemiology”