Bayesian networks : with examples in R

Bayesian networks with examples in R

Detalles Bibliográficos
Otros Autores: Scutari, Marco, author (author), Denis, Jean-Baptiste, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, Florida ; London, England ; New York : CRC Press [2022]
Edición:2nd ed
Colección:Chapman and Hall/CRC Texts in Statistical Science
Materias:
Bayesian statistical decision theory.
R (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757912406719
Tabla de Contenidos:
  • 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&amp
  • 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 1
  • 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.

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