Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2/e (Paperback)

Osvaldo Martin

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商品描述

An introduction to statistical modeling and probabilistic programming using PyMC3 and 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.
Code and figures
You canfind the code and figures in this GitHub repository github.com/aloctavodia/BAP/ You can also use this repository to reportany problem you find with the book or code


Book Description

The second edition of Bayesian Analysis with Python is an introductionto the main concepts of applied Bayesian inference and its practicalimplementation in Python using PyMC3, a state-of-the-art probabilisticprogramming library, and ArviZ a new library for exploratory analysis of Bayesian models.

    The main concepts of Bayesian statisticsare covered using a practical and computational approach. Synthetic andreal data sets are used to introduce several types of models such asgeneralized linear models for regression and classification, mixturemodels, hierarchical models and Gaussian process among others.

By the end of the book, you will have a working knowledge ofprobabilistic modeling and you will be able to design and implementBayesian models for your own data science problems. After reading thebook you will be better prepared to delve into more advance material orspecialized statistical modeling in case you need it.


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 under 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 in the natural orsocial sciences, or a developer looking to get started with Bayesiandata analysis and probabilistic programming, this book is for you. Thebook is introductory so no previous statistical knowledge is required,although some experience in using Python and NumPy is expected.

Table of Contents

 

 

  1. Thinking Probabilistically
  2. Programming Probabilistically
  3. Modeling with Linear Regression
  4. Generalizing Linear Models
  5. Model Comparison
  6. Mixture Models
  7. Gaussian Processes
  8. Inference Engines
  9. Where to go next?

 

商品描述(中文翻譯)

使用PyMC3和ArviZ進行統計建模和概率編程的介紹

主要特點:
- 逐步指南,使用PyMC3和ArviZ進行貝葉斯數據分析
- 現代、實用和計算方法的貝葉斯統計建模
- 通過示例問題和練習來學習貝葉斯分析和最佳實踐

代碼和圖表:
您可以在GitHub存儲庫github.com/aloctavodia/BAP/中找到代碼和圖表。您也可以使用此存儲庫報告書籍或代碼中的任何問題。

書籍描述:
《Python貝葉斯分析》第二版是應用貝葉斯推斷的主要概念及其在Python中的實際實現的介紹。使用PyMC3,一個先進的概率編程庫,以及ArviZ,一個用於探索性分析貝葉斯模型的新庫。

通過實際和計算方法介紹了貝葉斯統計的主要概念。使用合成和真實數據集來介紹多種模型,例如回歸和分類的廣義線性模型,混合模型,階層模型和高斯過程等。

閱讀本書後,您將具備概率建模的實際知識,並能夠為自己的數據科學問題設計和實施貝葉斯模型。閱讀本書後,您將更好地準備深入研究更高級的材料或需要的專業統計建模。

您將學到什麼:
- 使用Python庫PyMC3構建概率模型
- 使用ArviZ分析概率模型
- 獲取檢查模型和必要時修改模型的技能
- 理解階層模型的優點和注意事項
- 了解不同模型如何用於回答不同的數據分析問題
- 比較模型並選擇替代模型
- 發現不同模型如何在概率觀點下統一
- 以概率思維,並從貝葉斯框架的靈活性中受益

本書適合對貝葉斯數據分析和概率編程感興趣的學生、數據科學家、自然或社會科學的研究人員或開發人員。本書是入門級的,因此不需要先前的統計知識,但需要一些使用Python和NumPy的經驗。

目錄:
1. 概率思維
2. 概率編程
3. 線性回歸建模
4. 廣義線性模型
5. 模型比較
6. 混合模型
7. 高斯過程
8. 推斷引擎
9. 下一步該去哪裡?