Chapter 3. Introducing Bayesian Inference

In Chapter 1, Introducing the Probability Theory, we learned about the Bayes theorem as the relation between conditional probabilities of two random variables such as A and B. This theorem is the basis for updating beliefs or model parameter values in Bayesian inference, given the observations. In this chapter, a more formal treatment of Bayesian inference will be given. To begin with, let us try to understand how uncertainties in a real-world problem are treated in Bayesian approach.

Bayesian view of uncertainty

The classical or frequentist statistics typically take the view that any physical process-generating data containing noise can be modeled by a stochastic model with fixed values of parameters. The ...

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