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Monday, December 7 • 13:00 - 15:00
Monte Carlo Inference Methods

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"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.

Monday December 7, 2015 13:00 - 15:00
Level 2 room 210 AB

Attendees (19)