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Bayesian Reasoning and Machine Learning by David Barber

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1

Probabilistic reasoning

We have intuition about how uncertainty works in simple cases. To reach sensible conclusions in complicated situations, however – where there may be many (possibly) related events and many possible outcomes – we need a formal ‘calculus’ that extends our intuitive notions. The concepts, mathematical language and rules of probability give us the formal framework we need. In this chapter we review basic concepts in probability – in particular, conditional probability and Bayes’ rule, the workhorses of machine learning. Another strength of the language of probability is that it structures problems in a form consistent for computer implementation. We also introduce basic features of the BRMLTOOLBOX that support manipulating ...

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