Bayesian Analysis with Python - Second Edition: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ

Name: Bayesian Analysis with Python - Second Edition: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ
Brand: Atlantic Books
SKU: 9781789341652
Price: 4363.0 INR
Availability: InStock

by Osvaldo Martin

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ISBN13: 9781789341652
Binding: Paperback
Subject: N/A
Publisher: Packt Publishing
Publisher Imprint: Packt Publishing
Publication Date: December 26, 2018
Pages: 356
Original Price: GBP 35.99
Language: English
Edition: 2 Revised edition
Item Weight: 613 grams
BISAC Subject(s): Languages / Python

About The Book

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

Martin, Osvaldo: - Osvaldo Martin is a researcher at The National Scientific and Technical Research Council (CONICET), in Argentina. He has worked on structural bioinformatics of protein, glycans, and RNA molecules. He has experience using Markov Chain Monte Carlo methods to simulate molecular systems 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 core developers of PyMC3 and ArviZ.

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