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Introduction to Bayesian inference

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If you have a question about this talk, please contact Heidi Howard.

Probabilistic models allow us to make flexible and robust systems that handle uncertainty in our data gracefully. In a Bayesian approach we express prior beliefs over our model’s parameters, and update our beliefs by finding the posterior distribution over the parameters. In this lecture, we will consider how these models can be described graphically, and how efficient Bayesian inference can be used for training.

This talk is part of the Research Students Lecture Series series.

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