Bayesian Networks With Examples in R [2 ed.] 9780367366513, 9781000410396, 9780429347436, 9781032038490

Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Preface to the Second Edition
Preface to the First Edition
1 The Discrete Case: Multinomial Bayesian Networks
1.1 Introductory Example: Train-Use Survey
1.2 Graphical Representation
1.3 Probabilistic Representation
1.4 Estimating the Parameters: Conditional Probability Tables
1.5 Learning the DAG Structure: Tests and Scores
1.5.1 Conditional Independence Tests
1.5.2 Network Scores
1.6 Using Discrete Bayesian Networks
1.6.1 Using the DAG Structure
1.6.2 Using the Conditional Probability Tables
1.6.2.1 Exact Inference
1.6.2.2 Approximate Inference
1.7 Plotting Discrete Bayesian Networks
1.7.1 Plotting DAGs
1.7.2 Plotting Conditional Probability Distributions
1.8 Further Reading
2 The Continuous Case: Gaussian Bayesian Networks
2.1 Introductory Example: Crop Analysis
2.2 Graphical Representation
2.3 Probabilistic Representation
2.4 Estimating the Parameters: Correlation Coefficients
2.5 Learning the DAG Structure: Tests and Scores
2.5.1 Conditional Independence Tests
2.5.2 Network Scores
2.6 Using Gaussian Bayesian Networks
2.6.1 Exact Inference
2.6.2 Approximate Inference
2.7 Plotting Gaussian Bayesian Networks
2.7.1 Plotting DAGs
2.7.2 Plotting Conditional Probability Distributions
2.8 More Properties
2.9 Further Reading
3 The Mixed Case: Conditional Gaussian Bayesian Networks
3.1 Introductory Example: Healthcare Costs
3.2 Graphical and Probabilistic Representation
3.3 Estimating the Parameters: Mixtures of Regressions
3.4 Learning the DAG Structure: Tests and Scores
3.5 Using Conditional Gaussian Bayesian Networks
3.6 Further Reading
4 Time Series: Dynamic Bayesian Networks
4.1 Introductory Example: Domotics
4.2 Graphical Representation
4.3 Probabilistic Representation
4.4 Learning a Dynamic Bayesian Network
4.5 Using Dynamic Bayesian Networks
4.6 Plotting Dynamic Bayesian Networks
4.7 Further Reading
5 More Complex Cases: General Bayesian Networks
5.1 Introductory Example: A&E Waiting Times
5.2 Graphical and Probabilistic Representation
5.3 Building the Model in Stan
5.3.1 Generating Data
5.3.2 Exploring the Variables
5.4 Estimating the Parameters in Stan
5.5 Further Reading
6 Theory and Algorithms for Bayesian Networks
6.1 Conditional Independence and Graphical Separation
6.2 Bayesian Networks
6.3 Markov Blankets
6.4 Moral Graphs
6.5 Bayesian Network Learning
6.5.1 Structure Learning
6.5.1.1 Constraint-Based Algorithms
6.5.1.2 Score-Based Algorithms
6.5.1.3 Hybrid Algorithms
6.5.2 Parameter Learning
6.6 Bayesian Network Inference
6.6.1 Probabilistic Reasoning and Evidence
6.6.2 Algorithms for Belief Updating
6.6.2.1 Exact Inference Algorithms
6.6.2.2 Approximate Inference Algorithms
6.7 Causal Bayesian Networks
6.8 Evaluating a Bayesian Network
6.9 Further Reading
7 Software for Bayesian Networks
7.1 An Overview of R Packages
7.1.1 The deal Package
7.1.2 The catnet Package
7.1.3 The pcalg Package
7.1.4 The abn Package
7.2 Stan and BUGS Software Packages
7.2.1 Stan: A Feature Overview
7.2.2 Inference Based on MCMC Sampling
7.3 Other Software Packages
7.3.1 BayesiaLab
7.3.2 Hugin
7.3.3 GeNIe
8 Real-World Applications of Bayesian Networks
8.1 Learning Protein-Signalling Networks
8.1.1 A Gaussian Bayesian Network
8.1.2 Discretising Gene Expressions
8.1.3 Model Averaging
8.1.4 Choosing the Significance Threshold
8.1.5 Handling Interventional Data
8.1.6 Querying the Network
8.2 Predicting the Body Composition
8.2.1 Aim of the Study
8.2.2 Designing the Predictive Approach
8.2.2.1 Assessing the Quality of a Predictor
8.2.2.2 The Saturated BN
8.2.2.3 Convenient BNs
8.2.3 Looking for Candidate BNs
8.3 Further Reading
A Graph Theory
A.1 Graphs, Nodes and Arcs
A.2 The Structure of a Graph
A.3 Further Reading
B Probability Distributions
B.1 General Features
B.2 Marginal and Conditional Distributions
B.3 Discrete Distributions
B.3.1 Binomial Distribution
B.3.2 Multinomial Distribution
B.3.3 Other Common Distributions
B.3.3.1 Bernoulli Distribution
B.3.3.2 Poisson Distribution
B.4 Continuous Distributions
B.4.1 Normal Distribution
B.4.2 Multivariate Normal Distribution
B.4.3 Other Common Distributions
B.4.3.1 Chi-Square Distribution
B.4.3.2 Student's t Distribution
B.4.3.3 Beta Distribution
B.4.3.4 Dirichlet Distribution
B.5 Conjugate Distributions
B.6 Further Reading
C A Note about Bayesian Networks
C.1 Bayesian Networks and Bayesian Statistics
Glossary
Solutions
Bibliography
Index

Polecaj historie

Bayesian Networks: With Examples in R [2 ed.] 9780367366513, 9780429347436, 9781032038490

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet

4,224 343 19MB Read more

Bayesian Essentials with R

2,045 205 5MB Read more

Statistical rethinking: a Bayesian course with examples in R and Stan 9781482253467, 1482253461

1,198 224 11MB Read more

Statistical rethinking : a Bayesian course with examples in R and Stan 9781482253467, 1482253461

1,381 113 12MB Read more

Statistical Rethinking: A Bayesian Course with Examples in R and STAN [2 ed.] 036713991X, 9780367139919

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making

6,598 1,257 24MB Read more

Statistical Rethinking: A Bayesian Course with Examples in R and STAN [2 ed.]

933 95 10MB Read more

Mastering Bayesian Principles with R 3900051070

Mastering Bayesian Principles with R is an essential resource for those looking to delve into the world of Bayesian data

165 94 10MB Read more

Bayesian Essentials with R [2 ed.] 9781461486879, 1461486874

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most s

1,057 154 8MB Read more

Bayesian Essentials with R [2nd ed] 9781461486862, 9781461486879, 1461486866

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most s

1,521 234 9MB Read more

Bayesian Statistical Modeling with Stan, R, and Python 9789811947544, 9789811947551

765 250 10MB Read more