Mastering Probabilistic Graphical Models using Python

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

About This Book

• Gain in-depth knowledge of Probabilistic Graphical Models
• Model time-series problems using Dynamic Bayesian Networks
• A practical guide to help you apply PGMs to real-world problems

Who This Book Is For

If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.

What You Will Learn

• Get to know the basics of probability theory and graph theory
• Work with Markov networks
• Implement Bayesian networks
• Exact inference techniques in graphical models such as the variable elimination algorithm
• Understand approximate inference techniques in graphical models such as message passing algorithms
• Sampling algorithms in graphical models
• Grasp details of Naive Bayes with real-world examples
• Deploy probabilistic graphical models using various libraries in Python
• Gain working details of Hidden Markov models with real-world examples

In Detail

Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems.

Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks.

This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.

透過實際問題和Python的程式碼範例，深入學習概率圖模型

關於本書
- 深入了解概率圖模型
- 使用動態貝葉斯網絡建模時間序列問題
- 實用指南，幫助您將概率圖模型應用於實際問題

本書適合對貝葉斯學習或概率圖模型有基本了解的研究人員、機器學習愛好者或從事數據科學領域的人士，將幫助您了解圖模型的細節並在數據科學問題中應用它們。

您將學到什麼
- 了解概率理論和圖論的基礎知識
- 使用馬爾可夫網絡進行工作
- 實現貝葉斯網絡
- 圖模型中的精確推理技術，如變量消除算法
- 圖模型中的近似推理技術，如消息傳遞算法
- 圖模型中的抽樣算法
- 通過實際例子了解純粹貝葉斯
- 使用Python中的各種庫部署概率圖模型
- 通過實際例子瞭解隱馬爾可夫模型

詳細內容
概率圖模型是機器學習中一種使用圖論概念來簡潔表示和最佳預測數據問題的技術。

圖模型提供了在數據中尋找複雜模式的技術，並廣泛應用於語音識別、信息提取、圖像分割和建模基因調控網絡等領域。

本書從概率理論和圖論的基礎知識開始，然後討論了各種模型和推理算法。所有不同類型的模型都在書中討論，並提供了代碼範例來創建和修改它們，並在它們上運行不同的推理算法。書中還有一整章專門介紹了純粹貝葉斯模型和隱馬爾可夫模型，並使用了實際例子進行了詳細討論。