Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition

Martin, Osvaldo

ISBN 10: 1789341655 / ISBN 13: 9781789341652

Published by Packt Publishing, 2018

About this Item

All pages and cover are intact. Dust jacket included if applicable, though it may be missing on hardcover editions. Spine and cover may show minor signs of wear including scuff marks, curls or bends to corners as well as cosmetic blemishes including stickers. Pages may contain limited notes or highlighting. "From the library of" labels may be present. Shrink wrap, dust covers, or boxed set packaging may be missing. Bundled media e.g., CDs, DVDs, access codes may not be included.

Seller Inventory # COLV.1789341655.G

Bibliographic Details

• Title: Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition
• Publisher: Packt Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 1789341655
• ISBN 13: 9781789341652
• Binding: Soft cover
• Edition: 2nd Edition
• Condition: good

About this title

Synopsis

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ

Key Features

• A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ
• A modern, practical and computational approach to Bayesian statistical modeling
• A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.

Book Description

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.

What you will learn

• Build probabilistic models using the Python library PyMC3
• Analyze probabilistic models with the help of ArviZ
• Acquire the skills required to sanity check models and modify them if necessary
• Understand the advantages and caveats of hierarchical models
• Find out how different models can be used to answer different data analysis questions
• Compare models and choose between alternative ones
• Discover how different models are unified from a probabilistic perspective
• Think probabilistically and benefit from the flexibility of the Bayesian framework

Who this book is for

If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

About the Author

Osvaldo Martin is a researcher at CONICET, in Argentina. He has worked on structural bioinformatics of protein, glycans and RNA. He has experience using Markov Chain Monte Carlo methods to simulate molecules and loves to use Python to solve data analysis problems. He has taught courses about structural bioinformatics, Data Science and Bayesian data analysis. He was also the head of the organizing committee of PyData San Luis (Argentina) 2017. He is one of the developers of PyMC3 and ArviZ.