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Introduction to Bayesian inference

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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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