Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Bayesian Analysis with Python
  • Toc
  • feedback
Bayesian Analysis with Python

Bayesian Analysis with Python

By : Osvaldo Martin
3.2 (17)
close
Bayesian Analysis with Python

Bayesian Analysis with Python

3.2 (17)
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)
close
close
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Thinking Probabilistically
Thinking Probabilistically
Statistics, models, and this book's approach
Probability theory
Single-parameter inference
Communicating a Bayesian analysis
Posterior predictive checks
Summary
Exercises
2
Programming Probabilistically
Programming Probabilistically
Probabilistic programming
PyMC3 primer
Summarizing the posterior
Gaussians all the way down
Groups comparison
Hierarchical models
Summary
Exercises
3
Modeling with Linear Regression
Modeling with Linear Regression
Simple linear regression
Robust linear regression
Hierarchical linear regression
Polynomial regression
Multiple linear regression
Variable variance
Summary
Exercises
4
Generalizing Linear Models
Generalizing Linear Models
Generalized linear models
Logistic regression
Multiple logistic regression
Poisson regression
Robust logistic regression
The GLM module
Summary
Exercises
5
Model Comparison
chevron up
Model Comparison
Posterior predictive checks
Occam's razor – simplicity and accuracy
Information criteria
Bayes factors
Regularizing priors
WAIC in depth
Summary
Exercises
6
Mixture Models
Mixture Models
Mixture models
Finite mixture models
Non-finite mixture model
Continuous mixtures
Summary
Exercises
7
Gaussian Processes
Gaussian Processes
Linear models and non-linear data
Modeling functions
Gaussian process regression
Regression with spatial autocorrelation
Gaussian process classification
Cox processes
Summary
Exercises
8
Inference Engines
Inference Engines
Inference engines
Non-Markovian methods
Markovian methods
Diagnosing the samples
Summary
Exercises
9
Where To Go Next?
Where To Go Next?
10
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Model Comparison

"A map is not the territory it represents, but, if correct, it has a similar structure to the territory."
-Alfred Korzybski

Models should be designed as approximations to help us understand a particular problem, or a class of related problems. Models are not designed to be verbatim copies of the real world. Thus, all models are wrong in the same sense that maps are not the territory. Even when a priori, we consider every model to be wrong, not every model is equally wrong; some models will be better than others at describing a given problem. In the foregoing chapters, we focused our attention on the inference problem, that is, how to learn values of parameters from the data. In this chapter, we are going to focus on a complementary problem: how to compare two or more models that are used to explain the same data. As we will learn, this is not a simple...

End of Section 1
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete