Bayesian Networks

Bayesian Networks

Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. 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 and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.


Author
Publisher CRC Press
Release Date
ISBN 1482225581
Pages 243 pages
Bayesian Networks
Language: en
Pages: 243
Authors: Marco Scutari
Categories: Computers
Type: BOOK - Published: 2014-06-20 - Publisher: CRC Press

Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks u
Bayesian Networks in R
Language: en
Pages: 157
Authors: Radhakrishnan Nagarajan
Categories: Computers
Type: BOOK - Published: 2014-07-08 - Publisher: Springer Science & Business Media

Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inf
Gated Bayesian Networks
Language: en
Pages: 213
Authors: Marcus Bendtsen
Categories:
Type: BOOK - Published: 2017-06-08 - Publisher: Linköping University Electronic Press

Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graph
Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science
Language: en
Pages: 472
Authors: Franco Taroni
Categories: Mathematics
Type: BOOK - Published: 2014-09-22 - Publisher: John Wiley & Sons

Bayesian Networks “This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.” Dr. I
Advances in Bayesian Networks
Language: en
Pages: 328
Authors: José A. Gámez
Categories: Mathematics
Type: BOOK - Published: 2013-06-29 - Publisher: Springer

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within ar
Bayesian Networks
Language: en
Pages: 274
Authors: Marco Scutari
Categories: Computers
Type: BOOK - Published: 2021-07-22 - Publisher: CRC Press

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each ste
Bayesian Networks and Decision Graphs
Language: en
Pages: 448
Authors: Thomas Dyhre Nielsen
Categories: Science
Type: BOOK - Published: 2009-03-17 - Publisher: Springer Science & Business Media

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bay
Modeling and Reasoning with Bayesian Networks
Language: en
Pages: 561
Authors: Adnan Darwiche
Categories: Computers
Type: BOOK - Published: 2009-04-06 - Publisher: Cambridge University Press

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of tec
Bayesian Network
Language: en
Pages: 446
Authors: Ahmed Rebai
Categories: Mathematics
Type: BOOK - Published: 2010-08-18 - Publisher: BoD – Books on Demand

Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and
Bayesian Networks in Educational Assessment
Language: en
Pages: 662
Authors: Russell G. Almond
Categories: Social Science
Type: BOOK - Published: 2015-03-10 - Publisher: Springer

Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences.