From 14:00 - 15:00: Workshop (probably in room 1111-100)

**Abstract:**

Probabilistic Programming is the next big thing in machine learning, next to Deep Learning and Big Data. By combining general purpose programming with probabilistic modelling, probabilistic programming makes it possible to perform complex statistical reasoning with minimum programming efforts and high computational efficiency. The main idea is (a) to formulate a computer program, often using a standard programming language such as Python, that (b) models a specific problem of inference (medical, financial and so on) and (c) perform statistical inference simply by executing the program. This has been made possible by recent breakthroughs in automated inference (including automated advanced sampling methods and variational inference) and numerical computing (including large software frameworks such as Theano and Tensorflow). Probabilistic programming makes it even possible to implement Deep Learning methods that are based on Bayesian principles and that deliver reliable estimates of uncertainty. By decoupling statistical modelling (ie. formulating a question) from statistical inference (ie. answering the question), probabilistic programming is expected to dramatically extend the scope of probabilistic reasoning in technology, science, finance, society and medicine.

Webpage of Thomas Hamelryck:

http://www.binf.ku.dk/research/structural_bioinformatics/