Bayesian Analysis with Python

Osvaldo Martin

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商品描述

Key Features

  • Simplify the Bayes process for solving complex statistical problems using Python;
  • Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises;
  • Learn how and when to use Bayesian analysis in your applications with this guide.

Book Description

The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.

What you will learn

  • Understand the essentials Bayesian concepts from a practical point of view
  • Learn how to build probabilistic models using the Python library PyMC3
  • Acquire the skills to sanity-check your models and modify them if necessary
  • Add structure to your models and get the advantages of hierarchical models
  • Find out how different models can be used to answer different data analysis questions
  • When in doubt, learn to choose between alternative models.
  • Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression.
  • Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework

About the Author

Osvaldo Martin is a researcher at The National Scientific and Technical Research Council (CONICET), the main organization in charge of the promotion of science and technology in Argentina. He has worked on structural bioinformatics and computational biology problems, especially on how to validate structural protein models. He has experience in 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, Python programming, and, more recently, Bayesian data analysis. Python and Bayesian statistics have transformed the way he looks at science and thinks about problems in general. Osvaldo was really motivated to write this book to help others in developing probabilistic models with Python, regardless of their mathematical background. He is an active member of the PyMOL community (a C/Python-based molecular viewer), and recently he has been making small contributions to the probabilistic programming library PyMC3.

Table of Contents

  1. Thinking Probabilistically - A Bayesian Inference Primer
  2. Programming Probabilistically – A PyMC3 Primer
  3. Juggling with Multi-Parametric and Hierarchical Models
  4. Understanding and Predicting Data with Linear Regression Models
  5. Classifying Outcomes with Logistic Regression
  6. Model Comparison
  7. Mixture Models
  8. Gaussian Processes

商品描述(中文翻譯)

主要特點


  • 使用Python簡化貝葉斯過程,解決複雜的統計問題;

  • 教學指南,通過示例問題和練習題引導您進行貝葉斯分析的學習之旅;

  • 通過本指南學習何時何地應用貝葉斯分析於您的應用程序。

書籍描述

本書的目的是教授貝葉斯數據分析的主要概念。我們將學習如何有效地使用PyMC3,一個用於概率編程的Python庫,進行貝葉斯參數估計、檢查模型並驗證模型。本書首先介紹貝葉斯框架的關鍵概念以及從實際角度看這種方法的主要優勢。接下來,我們將探索廣義線性模型的強大靈活性,以及如何將其適應各種問題,包括回歸和分類。我們還將研究混合模型和聚類數據,最後介紹非參數模型和高斯過程等高級主題。通過Python和PyMC3的幫助,您將學習實施、檢查和擴展貝葉斯模型以解決數據分析問題。

您將學到什麼


  • 從實際角度理解貝葉斯概念的基本要素

  • 學習使用Python庫PyMC3構建概率模型

  • 獲得檢查模型並在必要時進行修改的技能

  • 為模型添加結構,並獲得層次模型的優勢

  • 了解不同模型如何用於回答不同的數據分析問題

  • 在疑惑時,學習在替代模型之間做出選擇。

  • 使用回歸分析預測連續目標結果,使用邏輯回歸和softmax回歸分配類別

  • 學會以概率思考,發揮貝葉斯框架的威力和靈活性

關於作者

Osvaldo Martin是阿根廷國家科學技術研究委員會(CONICET)的研究員,該組織是阿根廷科學技術推廣的主要機構。他曾從事結構生物信息學和計算生物學問題的研究,尤其是如何驗證結構蛋白模型。他有使用馬爾可夫鏈蒙特卡羅方法模擬分子的經驗,並喜歡使用Python解決數據分析問題。他曾教授結構生物信息學、Python編程以及最近的貝葉斯數據分析課程。Python和貝葉斯統計學改變了他對科學的看法,並改變了他對問題的思考方式。Osvaldo非常有動力寫這本書,幫助其他人使用Python開發概率模型,無論他們的數學背景如何。他是PyMOL社區(一個基於C/Python的分子查看器)的活躍成員,最近他一直在對概率編程庫PyMC3做出一些小貢獻。

目錄


  1. 以概率思考 - 貝葉斯推論入門

  2. 以概率編程 - PyMC3入門

  3. 處理多參數和層次模型

  4. 理解和預測線性回歸模型的數據

  5. 使用邏輯回歸進行分類

  6. 模型比較

  7. 混合模型

  8. 高斯過程