## 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

## 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

## 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

## 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