Bayesian networks

• Verfasst von: Scutari, Marco [VerfasserIn]
• Verfasst von: Denis, Jean-Baptiste [VerfasserIn]
• Titel: Bayesian networks
• Titelzusatz: with examples in R
• Verf.angabe: Marco Scutari, Jean-Baptiste Denis
• Ausgabe: Second edition.
• Verlagsort: Boca Raton
• Verlag: Chapman & Hall/CRC
• Jahr: 2021
• Umfang: 1 online resource
• Illustrationen: illustrations (black and white).
• Gesamttitel/Reihe: Chapman & Hall/CRC texts in statistical science series
• Fussnoten: 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. - Description based on CIP data; resource not viewed
• ISBN: 978-1-000-41039-6
• ISBN: 1-000-41039-0
• ISBN: 978-1-000-41038-9
• ISBN: 1-000-41038-2
• ISBN: 978-0-429-34743-6
• ISBN: 0-429-34743-X
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Théorie de la décision bayésienne
• Sach-SW: R (Langage de programmation) ; Méthodes statistiques
• Sach-SW: Bayesian statistical decision theory
• K10plus-PPN: 1859057357
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4373598606
• Lokale URL Inst.: Zum Volltext

Abstract

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation. The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R packages and other software implementing Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein-signalling network published in Science and a probabilistic graphical model for predicting the composition of different body parts. Covering theoretical and practical aspects of Bayesian networks, this book provides you with an introductory overview of the field. It gives you a clear, practical understanding of the key points behind this modelling approach and, at the same time, it makes you familiar with the most relevant packages used to implement real-world analyses in R. The examples covered in the book span several application fields, data-driven models and expert systems, probabilistic and causal perspectives, thus giving you a starting point to work in a variety of scenarios. Online supplementary materials include the data sets and the code used in the book, which will all be made available from https://www.bnlearn.com/book-crc-2ed/.