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<!DOCTYPE html>
<html lang="en">
<head>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>BAP3</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 0;
background-color: #f5f5f5;
}
header {
background-color: #333;
color: #fff;
padding: 20px;
text-align: center;
}
.container {
max-width: 800px;
margin: auto;
padding: 20px;
}
h1 {
color: #f37043;
}
p {
line-height: 1.6;
color: #000000;
}
footer {
background-color: #333;
color: #fff;
padding: 10px 0;
text-align: center;
}
img {
max-width: 100%;
height: auto;
margin-right: 20px;
}
.about-section {
display: flex;
align-items: center;
justify-content: center;
flex-wrap: wrap;
margin-left: -55px;
}
.about-section p {
flex: 1;
margin-right: 10px;
margin-left: 45px;
}
.citation {
margin-top: 40px;
}
.citation pre {
background-color: #f9f9f9;
padding: 5px;
border-radius: 10px;
overflow-x: auto;
}
</style>
</head>
<body>
<header>
<h1>Bayesian Analysis with Python</h1>
</header>
<div class="container">
<div class="about-section">
<img src="cover.png" alt="Book Cover" style="max-width: 300px;">
<p>The third edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian
inference and its practical implementation in Python using state-of-the-art libraries like PyMC, ArviZ,
Bambi, PyMC-BART, PreliZ, and Kulprit.
<br>
<br>
By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you
to design and implement Bayesian models for your data science challenges. And you will well-prepared to delve
into more advanced material if the need arises.</p>
</div>
<br>
<br>
<h2>Table of Contents</h2>
<ul>
<li>Chapter 1: Thinking Probabilistically</li>
<li>Chapter 2: Programming Probabilistically</li>
<li>Chapter 3: Hierarchical Models</li>
<li>Chapter 4: Modeling with Lines</li>
<li>Chapter 5: Comparing Models</li>
<li>Chapter 6: Modeling with Bambi</li>
<li>Chapter 7: Mixture Models</li>
<li>Chapter 8: Gaussian Processes</li>
<li>Chapter 9: Bayesian Additive Regression Trees</li>
<li>Chapter 10: Inference Engines</li>
<li>Chapter 11: Where to Go Next</li>
</ul>
<h2>Get the Book</h2>
<p>You can get a copy of the book from:</p>
<ul>
<li><a href="https://packt.link/JrkVW">Amazon.com</a></li>
</ul>
<h2>Code</h2>
<p>All the code used in the book, including code to generate many of the images is available at
<a href="https://github.com/aloctavodia/BAP3">GitHub</a>
</p>
<h2>Solutions to exercises</h2>
<p>Solutions to the exercises can be found at
<a href="https://github.com/aloctavodia/BAP3/tree/main/exercises">GitHub</a>
</p>
<h2>Errors</h2>
<p>If you find an error in the book not listed on the <a href="https://github.com/aloctavodia/BAP3/blob/main/errata.md">errata</a>, or have questions, please fill an <a href="https://github.com/aloctavodia/BAP3/issues">issue</a> in the book's GitHub repository
</p>
<div class="citation">
<h2>Citation</h2>
<p>If you use this book in your own work, please cite it using:
<em>Martin Osvaldo A, Bayesian Analysis with Python. Packt Publishing. 2024. ISBN 978-1-80512-716-1
</em>
<br>
<br>
Here is the citation in BibTeX format</p>
<pre>
@book{martin_bap_2024,
title = {Bayesian {Analysis} with {Python}: {A} {Practical} {Guide} to probabilistic modeling, 3rd {Edition}},
isbn = {978-1-80512-716-1},
shorttitle = {Bayesian {Analysis} with {Python}},
language = {English},
publisher = {Packt Publishing},
author = {Martin, Osvaldo A},
month = feb,
year = {2024},
}
</pre>
</div>
<h2>Donations</h2>
<p>This book is only possible because of open-source contributors.
If you finds this tools useful you can donate to <a href="https://numfocus.org/donate-to-arviz">ArviZ</a>
or <a href="https://numfocus.org/donate-to-pymc">PyMC</a> to help sustain their ongoing development..
</p>
<p>If instead, you want to donate directly to me, the author, please do it at <a href="https://ko-fi.com/aloctavodia">ko-fi</a>
</p>
</div>
<footer>
<p><a href=https://aloctavodia.github.io style="color:#f37043;">More about the author</a></p>
</footer>
</body>
</html>