Book Image

Bayesian Analysis with Python - Second Edition

By : Osvaldo Martin
4.5 (2)
Book Image

Bayesian Analysis with Python - Second Edition

4.5 (2)
By: Osvaldo Martin

Overview of this book

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.
Table of Contents (11 chapters)
9
Where To Go Next?

Gaussian process classification

Gaussian processes are not restricted to regression. We can also use them for classification. As we saw in Chapter 4, Generalizing Linear Models, we turn a linear model into a suitable model to classify data by using a Bernoulli likelihood with a logistic inverse link function (and then applying a boundary decision rule to separate classes). We will try to recapitulate model_0 from Chapter 4, Generalizing Linear Models, for the iris dataset, this time using a GP instead of a linear model.

Let's invite the iris dataset to the stage one more time:

iris = pd.read_csv('../data/iris.csv')
iris.head()
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa

We are going to begin with the simplest...