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Tutorial
Monte Carlo Inference Methods
Iain Murray

Mon Dec 07 10:00 AM -- 12:00 PM (PST) @ Level 2 room 210 AB

"Monte Carlo" methods use random sampling to understand a system, estimate averages, or compute integrals. Monte Carlo methods were amongst the earliest applications run on electronic computers in the 1940s, and continue to see widespread use and research as our models and computational power grow. In the NIPS community, random sampling is widely used within optimization methods, and as a way to perform inference in probabilistic models. Here "inference" simply means obtaining multiple plausible settings of model parameters that could have led to the observed data. Obtaining a range of explanations tells us both what we can and cannot know from our data, and prevents us from making overconfident (wrong) predictions.

This introductory-level tutorial will describe some of the fundamental Monte Carlo algorithms, and examples of how they can be combined with models in different ways. We'll see that Monte Carlo methods are sometimes a quick and easy way to perform inference in a new model, but also what can go wrong, and some treatment of how to debug these randomized algorithms.

Author Information

Iain Murray (University of Edinburgh)

Iain Murray is a SICSA Lecturer in Machine Learning at the University of Edinburgh. Iain was introduced to machine learning by David MacKay and Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His thesis on Monte Carlo methods received an honourable mention for the ISBA Savage Award. He was a commonwealth fellow in Machine Learning at the University of Toronto, before moving to Edinburgh in 2010. Iain's research interests include building flexible probabilistic models of data, and probabilistic inference from indirect and uncertain observations. Iain is passionate about teaching. He has lectured at several Summer schools, is listed in the top 15 authors on videolectures.net, and was awarded the EUSA Van Heyningen Award for Teaching in Science and Engineering in 2015.

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