[كتاب][B] Bayesian artificial intelligence
KB Korb, AE Nicholson - 2010 - books.google.com
The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …
main concepts, foundation, and applications of Bayesian networks. This edition contains a …
Bayesian networks and influence diagrams
This book is a monograph on practical aspects of probabilistic networks (aka probabilistic
graphical models) and is intended to provide a comprehensive guide for practitioners that …
graphical models) and is intended to provide a comprehensive guide for practitioners that …
Bayesian networks in biomedicine and health-care
Physiological mechanisms in human biology, the progress of disease in individual patients,
hospital work-flow management: these are just a few of the many complicated processes …
hospital work-flow management: these are just a few of the many complicated processes …
From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support
Objectives (1) To develop a rigorous and repeatable method for building effective Bayesian
network (BN) models for medical decision support from complex, unstructured and …
network (BN) models for medical decision support from complex, unstructured and …
Properties of sensitivity analysis of Bayesian belief networks
The assessments for the various conditional probabilities of a Bayesian belief network
inevitably are inaccurate, influencing the reliability of its output. By subjecting the network to …
inevitably are inaccurate, influencing the reliability of its output. By subjecting the network to …
Information science and statistics
Untitled Page 1 Page 2 Information Science and Statistics Series Editors: M. Jordan J.
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …
When do numbers really matter?
Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a
belief network do not matter much for the results of probabilistic queries. Yet, one can …
belief network do not matter much for the results of probabilistic queries. Yet, one can …
A distance measure for bounding probabilistic belief change
We propose a distance measure between two probability distributions, which allows one to
bound the amount of belief change that occurs when moving from one distribution to …
bound the amount of belief change that occurs when moving from one distribution to …
Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems
OBJECTIVE: One of the hardest technical tasks in employing Bayesian network models in
practice is obtaining their numerical parameters. In the light of this difficulty, a pressing …
practice is obtaining their numerical parameters. In the light of this difficulty, a pressing …
You only derive once (YODO): automatic differentiation for efficient sensitivity analysis in Bayesian networks
Sensitivity analysis measures the influence of a Bayesian network's parameters on a
quantity of interest defined by the network, such as the probability of a variable taking a …
quantity of interest defined by the network, such as the probability of a variable taking a …