O'Reilly logo

Learning Bayesian Models with R by Dr. Hari M. Koduvely

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

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 ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required