Chapter 1. Graphical Models and Probabilistic Reasoning
Published Online: 25 SEP 2009
DOI: 10.1002/9780470684023.ch1
Copyright © 2009 John Wiley & Sons, Ltd
Book Title
Bayesian Networks: An Introduction
Additional Information
How to Cite
Koski, T. and Noble, J. M. (2009) Graphical Models and Probabilistic Reasoning, in Bayesian Networks: An Introduction, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470684023.ch1
Publication History
- Published Online: 25 SEP 2009
- Published Print: 25 SEP 2009
Book Series:
Book Series Editors:
- Walter A. Shewhart,
- Samuel S. Wilks
ISBN Information
Print ISBN: 9780470743041
Online ISBN: 9780470684023
- Summary
- Chapter
Keywords:
- graphical models and probabilistic reasoning;
- graphical models and probability theory and graph theory interaction;
- Bayesian networks and joint probability models;
- correlations or statistical associations between variables implying causation;
- axioms of probability and basic notations;
- random variables and random vectors;
- Bayes update of probability;
- task of inductive learning;
- multinomial sampling and Dirichlet integral;
- predictive probability for next toss
Summary
This chapter contains sections titled:
Introduction
Axioms of probability and basic notations
The Bayes update of probability
Inductive learning
Interpretations of probability and Bayesian networks
Learning as inference about parameters
Bayesian statistical inference
Tossing a thumb-tack
Multinomial sampling and the Dirichlet integral
Notes
Exercises: Probabilistic theories of causality, Bayes' rule, multinomial sampling and the Dirichlet density