Building Probabilistic Graphical Models with Python
Год издания: 2014
Автор: Kiran R Karkera
Издательство: Packt Publishing
ISBN: 9781783289004
Язык: Английский
Формат: PDF
Качество: Изначально компьютерное (eBook)
Интерактивное оглавление: Да
Количество страниц: 173
Описание: With the increasing prominence in machine learning and data science applications, probabilistic graphical models are a new tool that machine learning users can use to discover and analyze structures in complex problems. The variety of tools and algorithms under the PGM framework extend to many domains such as natural language processing, speech processing, image processing, and disease diagnosis.
You've probably heard of graphical models before, and you're keen to try out new landscapes in the machine learning area. This book gives you enough background information to get started on graphical models, while keeping the math to a minimum.
Оглавление
1: Probability
The theory of probability
Goals of probabilistic inference
Conditional probability
The chain rule
The Bayes rule
Interpretations of probability
Random variables
Marginal distribution
Joint distribution
Independence
Conditional independence
Types of queries
Summary
2: Directed Graphical Models
Graph terminology
Independence and independent parameters
The Bayes network
Reasoning patterns
D-separation
Factorization and I-maps
The Naive Bayes model
Summary
3: Undirected Graphical Models
Pairwise Markov networks
The Gibbs distribution
An induced Markov network
Factorization
Flow of influence
Active trail and separation
Structured prediction
The factorization-independence tango
Summary
4: Structure Learning
The structure learning landscape
Constraint-based structure learning
Score-based learning
Summary
5: Parameter Learning
The likelihood function
Parameter learning example using MLE
MLE for Bayesian networks
Bayesian parameter learning example using MLE
Data fragmentation
Effects of data fragmentation on parameter estimation
Bayesian parameter estimation
Bayesian estimation for the Bayesian network
Example of Bayesian estimation
Summary
6: Exact Inference Using Graphical Models
Complexity of inference
Using the Variable Elimination algorithm
The tree algorithm
Summary
7: Approximate Inference Methods
The optimization perspective
Steps in the LBP algorithm
Sampling-based methods
Summary
