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Building Probabilistic Graphical Models with Python by Kiran R Karkera

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Chapter 1. Probability

Before we embark on the journey through the land of graphical models, we must equip ourselves with some tools that will aid our understanding. We will first start with a tour of probability and its concepts such as random variables and the types of distributions.

We will then try to understand the types of questions that probability can help us answer and the multiple interpretations of probability. Finally, we will take a quick look at the Bayes rule, which helps us understand the relationships between probabilities, and also look at the accompanying concepts of conditional probabilities and the chain rule.

The theory of probability

We often encounter situations where we have to exercise our subjective belief about an event's ...

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