Welcome to the online version Bayesian Modeling and Computation in Python. If you’d like a physical copy it can purchased from the publisher here or on Amazon. This site contains an …

At a high-level we can describe the process of constructing Bayesian modeling in 3 steps. Given some data and some assumptions on how this data could have been generated, we design a …

Approximate Bayesian Computation# In this chapter we discuss Approximate Bayesian Computation (ABC). The “approximate” in ABC refers to the lack of explicit likelihood, but not …

In this chapter we will discuss some Bayesian approaches to this problem. We will start by considering time series modeling as a regression problem, with the design matrices parsed …

In this book we will mainly use the Python library ArviZ [3] to help us perform exploratory analysis of Bayesian models. In a real life setting, Bayesian inference and exploratory analysis of …

This is a reference notebook for the book Bayesian Modeling and Computation in Python. The textbook is not needed to use or run this code, though the context and explanation is missing …

So far we have explored a couple of examples to gain intuition into what splines are and how to automate their creation with the help of Patsy. We can now move forward into computing the …

By the end of this chapter you will be more comfortable with linear models, more familiar with many of the steps in a Bayesian workflow, and more comfortable conducting Bayesian …

This is a reference notebook for the book Bayesian Modeling and Computation in Python. The textbook is not needed to use or run this code, though the context and explanation is missing …

The purpose of PPLs is help Bayesian practitioners build generative models to solve problem at hand, for example, perform inference of a Bayesian model through estimating the posterior …