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Bayesian Nets and Causality: Philosophical and Computational Foundations

Jon Williamson

Published in print:
2004
Published Online:
September 2007
ISBN:
9780198530794
eISBN:
9780191712982
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198530794.001.0001
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy

This book provides an introduction to, and analysis of, the use of Bayesian nets in causal modelling. It puts forward new conceptual foundations for causal network modelling: The book argues that ... More


Inference Networks: Bayes and Wigmore

PHILIP DAWID, DAVID SCHUM, and AMANDA HEPLER

in Evidence, Inference and Enquiry

Published in print:
2011
Published Online:
January 2013
ISBN:
9780197264843
eISBN:
9780191754050
Item type:
chapter
Publisher:
British Academy
DOI:
10.5871/bacad/9780197264843.003.0005
Subject:
Sociology, Methodology and Statistics

Methods for performing complex probabilistic reasoning tasks, often based on masses of different forms of evidence obtained from a variety of different sources, are being sought by, and developed ... More


BAYESIAN NETS

Jon Williamson

in Bayesian Nets and Causality: Philosophical and Computational Foundations

Published in print:
2004
Published Online:
September 2007
ISBN:
9780198530794
eISBN:
9780191712982
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198530794.003.0003
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy

This chapter introduces Bayesian networks and probabilistic independence, and shows how Bayesian nets are used to represent probability functions. Inference in Bayesian nets is discussed and the ... More


Nonparametric Bayesian Networks *

Katja Ickstadt, Bjöorn Bornkamp, Marco Grzegorczyk, Jakob Wieczorek, Malik R. Sheriff, Hernáan E. Grecco, and Eli Zamir

in Bayesian Statistics 9

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199694587
eISBN:
9780191731921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199694587.003.0010
Subject:
Mathematics, Probability / Statistics

A convenient way of modelling complex interactions is by employing graphs or networks which correspond to conditional independence structures in an underlying statistical model. One main class of ... More


When are graphical causal models not good models?

Jan Lemeire, Kris Steenhaut, and Abdellah Touhafi

in Causality in the Sciences

Published in print:
2011
Published Online:
September 2011
ISBN:
9780199574131
eISBN:
9780191728921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199574131.003.0027
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy

The principle of Kolmogorov minimal sufficient statistic (KMSS) states that the meaningful information of data is given by the regularities in the data. The KMSS is the minimal model that describes ... More


Why making Bayesian networks objectively Bayesian makes sense

Dawn E. Holmes

in Causality in the Sciences

Published in print:
2011
Published Online:
September 2011
ISBN:
9780199574131
eISBN:
9780191728921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199574131.003.0028
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy

It is well‐known that Bayesian networks are so‐called because of their use of Bayes theorem for probabilistic inference. However, since Bayesian networks commonly use frequentist probabilities ... More


Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning

Christine Sinoquet

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0002
Subject:
Mathematics, Probability / Statistics, Biostatistics

The aim of this chapter is to offer an advanced tutorial to scientists with no background or no deep background on probabilistic graphical models. To readers more familiar with these models, this ... More


Differences from Other Formal Theories

Neil Tennant

in Changes of Mind: An Essay on Rational Belief Revision

Published in print:
2012
Published Online:
September 2012
ISBN:
9780199655755
eISBN:
9780191742125
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199655755.003.0011
Subject:
Philosophy, Logic/Philosophy of Mathematics, Metaphysics/Epistemology

This chapter compares and contrasts the account with three other major formal accounts of belief revision: AGM-theory; Justified Truth-Maintenance Systems; and Bayesian networks. There are both ... More


Reliability

Luc Bovens and Stephan Hartmann

in Bayesian Epistemology

Published in print:
2004
Published Online:
January 2005
ISBN:
9780199269754
eISBN:
9780191601705
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/0199269750.003.0004
Subject:
Philosophy, Metaphysics/Epistemology

Introduces different interpretations of witness reliability into the models and constructs Bayesian-Network representations. Applies the models to Condorcet-style jury voting and Tversky and ... More


Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge

Jee Young Moon, Elias Chaibub Neto, Xinwei Deng, and Brian S. Yandell

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0007
Subject:
Mathematics, Probability / Statistics, Biostatistics

In a segregating population, quantitative trait loci (QTL) mapping can identify QTLs with a causal effect on a phenotype. A common feature of these methods is that QTL mapping and phenotype network ... More


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