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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


Hierarchical Bayesian Models

N. Thompson Hobbs and Mevin B. Hooten

in Bayesian Models: A Statistical Primer for Ecologists

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691159287
eISBN:
9781400866557
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691159287.003.0006
Subject:
Biology, Ecology

This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins ... 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


Solutions

N. Thompson Hobbs and Mevin B. Hooten

in Bayesian Models: A Statistical Primer for Ecologists

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691159287
eISBN:
9781400866557
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691159287.003.0012
Subject:
Biology, Ecology

This chapter provides solutions to the problems presented in the preceding chapter. It presents the diagrams for each problem as well as some explanations on how the solutions are arrived at. As has ... 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 Epistemology

Luc Bovens and Stephan Hartmann

Published in print:
2004
Published Online:
January 2005
ISBN:
9780199269754
eISBN:
9780191601705
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/0199269750.001.0001
Subject:
Philosophy, Metaphysics/Epistemology

Probabilistic models have much to offer to epistemology and philosophy of science. Arguably, the coherence theory of justification claims that the more coherent a set of propositions is, the more ... 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


Causal Models: How People Think about the World and Its Alternatives

Steven Sloman

Published in print:
2005
Published Online:
January 2007
ISBN:
9780195183115
eISBN:
9780199870950
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195183115.001.0001
Subject:
Philosophy, Philosophy of Mind

Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. ... 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


Problems

N. Thompson Hobbs and Mevin B. Hooten

in Bayesian Models: A Statistical Primer for Ecologists

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691159287
eISBN:
9781400866557
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691159287.003.0011
Subject:
Biology, Ecology

This chapter provides a set of structured problems to hone the reader's skills in model building. Each problem requires the reader to draw a Bayesian network and write the posterior and joint ... More


A new causal power theory

Kevin B. Korb, Erik P. Nyberg, and Lucas Hope

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/9780199574131.003.0030
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy

The causal power of C over E is (roughly) the degree to which changes in C cause changes in E. A formal measure of causal power would be very useful, as an aid to understanding and modelling complex ... More


Comparison of Mixture Bayesian and Mixture Regression Approaches to Infer Gene Networks

Sandra L. Rodriguez–Zas and Bruce R. Southey

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/9780198709022.003.0004
Subject:
Mathematics, Probability / Statistics, Biostatistics

Most Bayesian network applications to gene network reconstruction assume a single distributional model across all the samples and treatments analyzed. This assumption is likely to be unrealistic ... More


Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision

Péter Antal, András Millinghoffer, Gábor Hullám, Gergely Hajós, Péter Sárközy, András Gézsi, Csaba Szalai, and András Falus

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/9780198709022.003.0013
Subject:
Mathematics, Probability / Statistics, Biostatistics

The relative scarcity of the results reported by genetic association studies (GAS) prompted many research directions. Despite the centrality of the concept of association in GASs, refined concepts of ... More


Theory Unification and Graphical Models in Human Categorization

David Danks

in Causal Learning: Psychology, Philosophy, and Computation

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.003.0012
Subject:
Psychology, Developmental Psychology

Many different, seemingly mutually exclusive, theories of categorization have been proposed in recent years. The most notable theories have been those based on prototypes, exemplars, and causal ... More


Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Raphaël Mourad (ed.)

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.001.0001
Subject:
Mathematics, Probability / Statistics, Biostatistics

At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic ... More


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