Treffer: Bayesian networks
Titel:
Bayesian networks : with examples in R / Marco Scutari, Jean-Baptiste Denis
Ausgabe:
Second edition
Veröffentlicht:
Boca Raton : Chapman & Hall/CRC, 2021
Vertrieb:
Sebastopol, CA : O'Reilly Media Inc.
Umfang:
1 Online-Ressource : illustrations (black and white)
Publikationstyp:
Sprache:
Englisch
Schriftenreihe/Mehrbändiges Werk:
Chapman & Hall/CRC texts in statistical science series
Anmerkungen:
Previous edition: 2015
<P><STRONG>Preface to the Second Edition <BR>Preface to the First Edition</STRONG></P><P><STRONG>1. The Discrete Case: Multinomial Bayesian Networks <BR></STRONG> Introductory Example: Train Use Survey <BR> Graphical Representation <BR> Probabilistic Representation <BR> Estimating the Parameters: Conditional Probability Tables <BR> Learning the DAG Structure: Tests and Scores <BR> Conditional Independence Tests <BR> Network Scores <BR> Using Discrete Bayesian Networks <BR> Using the DAG Structure <BR> Using the Conditional Probability Tables <BR> Exact Inference <BR> Approximate Inference <BR> Plotting Discrete Bayesian Networks <BR> Plotting DAGs <BR> Plotting Conditional Probability Distributions <BR> Further Reading </P><P><STRONG> 2. The Continuous Case: Gaussian Bayesian Networks</STRONG> <BR> Introductory Example: Crop Analysis <BR> Graphical Representation <BR> Probabilistic Representation <BR> Estimating the Parameters: Correlation Coefficients <BR> Learning the DAG Structure: Tests and Scores <BR> Conditional Independence Tests <BR> Network Scores <BR> Using Gaussian Bayesian Networks <BR> Exact Inference <BR> Approximate Inference <BR> Plotting Gaussian Bayesian Networks <BR> Plotting DAGs <BR> Plotting Conditional Probability Distributions <BR> More Properties <BR> Further Reading </P><P><STRONG> 3. The Mixed Case: Conditional Gaussian Bayesian Networks</STRONG> <BR> Introductory Example: Healthcare Costs <BR> Graphical and Probabilistic Representation <BR> Estimating the Parameters: Mixtures of Regressions <BR> Learning the DAG Structure: Tests and Scores <BR> Using Conditional Gaussian Bayesian Networks <BR> Further Reading </P><P><STRONG> 4. Time Series: Dynamic Bayesian Networks</STRONG> <BR> Introductory Example: Domotics <BR> Graphical Representation <BR> Probabilistic Representation <BR> Learning a Dynamic Bayesian Network <BR> Using Dynamic Bayesian Networks <BR> Plotting Dynamic Bayesian Networks <BR> Further Reading </P><P><STRONG> 5. More Complex Cases: General Bayesian Networks</STRONG> <BR> Introductory Example: A&E Waiting Times <BR> Graphical and Probabilistic Representation <BR> Building the Model in Stan <BR> Generating Data &a
Includes bibliographical references and index
<P><STRONG>Preface to the Second Edition <BR>Preface to the First Edition</STRONG></P><P><STRONG>1. The Discrete Case: Multinomial Bayesian Networks <BR></STRONG> Introductory Example: Train Use Survey <BR> Graphical Representation <BR> Probabilistic Representation <BR> Estimating the Parameters: Conditional Probability Tables <BR> Learning the DAG Structure: Tests and Scores <BR> Conditional Independence Tests <BR> Network Scores <BR> Using Discrete Bayesian Networks <BR> Using the DAG Structure <BR> Using the Conditional Probability Tables <BR> Exact Inference <BR> Approximate Inference <BR> Plotting Discrete Bayesian Networks <BR> Plotting DAGs <BR> Plotting Conditional Probability Distributions <BR> Further Reading </P><P><STRONG> 2. The Continuous Case: Gaussian Bayesian Networks</STRONG> <BR> Introductory Example: Crop Analysis <BR> Graphical Representation <BR> Probabilistic Representation <BR> Estimating the Parameters: Correlation Coefficients <BR> Learning the DAG Structure: Tests and Scores <BR> Conditional Independence Tests <BR> Network Scores <BR> Using Gaussian Bayesian Networks <BR> Exact Inference <BR> Approximate Inference <BR> Plotting Gaussian Bayesian Networks <BR> Plotting DAGs <BR> Plotting Conditional Probability Distributions <BR> More Properties <BR> Further Reading </P><P><STRONG> 3. The Mixed Case: Conditional Gaussian Bayesian Networks</STRONG> <BR> Introductory Example: Healthcare Costs <BR> Graphical and Probabilistic Representation <BR> Estimating the Parameters: Mixtures of Regressions <BR> Learning the DAG Structure: Tests and Scores <BR> Using Conditional Gaussian Bayesian Networks <BR> Further Reading </P><P><STRONG> 4. Time Series: Dynamic Bayesian Networks</STRONG> <BR> Introductory Example: Domotics <BR> Graphical Representation <BR> Probabilistic Representation <BR> Learning a Dynamic Bayesian Network <BR> Using Dynamic Bayesian Networks <BR> Plotting Dynamic Bayesian Networks <BR> Further Reading </P><P><STRONG> 5. More Complex Cases: General Bayesian Networks</STRONG> <BR> Introductory Example: A&E Waiting Times <BR> Graphical and Probabilistic Representation <BR> Building the Model in Stan <BR> Generating Data &a
Includes bibliographical references and index
RVK-Notation:
Schlagworte:
ISBN:
9781000410396 ; 1000410390 ; 9781000410389 ; 1000410382 ; 9780429347436 ; 042934743X ; 9780367366513 (Sekundärausgabe)