# A Review of Bayesian Networks and Structure Learning

Mathematica Applicanda (2012)

- Volume: 40, Issue: 1
- ISSN: 1730-2668

## Access Full Article

top## Abstract

top## How to cite

topTimo J.T. Koski, and John Noble. "A Review of Bayesian Networks and Structure Learning." Mathematica Applicanda 40.1 (2012): null. <http://eudml.org/doc/292706>.

@article{TimoJ2012,

abstract = {This article reviews the topic of Bayesian networks. A Bayesian network is a factorisation of a probability distribution along a directed acyclic graph. The relation between graphical d-separation and independence is described. A short article by Arthur Cayley (1853) [7] is discussed, which laid ideas later used in Bayesian networks: factorisation, the noisy `or' gate, applications of algebraic geometry to Bayesian networks. The ideas behind Pearl's intervention calculus when the DAG represents a causal dependence structure; the relation between the work of Cayley and Pearl is commented on.Most of the discussion is about structure learning, outlining the two main approaches; search and score versus constraint based. Constraint based algorithms often rely on the assumption of faithfulness, that the data to which the algorithm is applied is generated from distributions satisfying a faithfulness assumption where graphical d- separation and independence are equivalent. The article presents some considerations for constraint based algorithms based on recent data analysis, indicating a variety of situations where the faithfulness assumption does not hold.},

author = {Timo J.T. Koski, John Noble},

journal = {Mathematica Applicanda},

keywords = {Bayesian networks, directed acyclic graph, Arthur Cayley, intervention calculus, graphical Markov model, Markov equivalence, structure learning},

language = {eng},

number = {1},

pages = {null},

title = {A Review of Bayesian Networks and Structure Learning},

url = {http://eudml.org/doc/292706},

volume = {40},

year = {2012},

}

TY - JOUR

AU - Timo J.T. Koski

AU - John Noble

TI - A Review of Bayesian Networks and Structure Learning

JO - Mathematica Applicanda

PY - 2012

VL - 40

IS - 1

SP - null

AB - This article reviews the topic of Bayesian networks. A Bayesian network is a factorisation of a probability distribution along a directed acyclic graph. The relation between graphical d-separation and independence is described. A short article by Arthur Cayley (1853) [7] is discussed, which laid ideas later used in Bayesian networks: factorisation, the noisy `or' gate, applications of algebraic geometry to Bayesian networks. The ideas behind Pearl's intervention calculus when the DAG represents a causal dependence structure; the relation between the work of Cayley and Pearl is commented on.Most of the discussion is about structure learning, outlining the two main approaches; search and score versus constraint based. Constraint based algorithms often rely on the assumption of faithfulness, that the data to which the algorithm is applied is generated from distributions satisfying a faithfulness assumption where graphical d- separation and independence are equivalent. The article presents some considerations for constraint based algorithms based on recent data analysis, indicating a variety of situations where the faithfulness assumption does not hold.

LA - eng

KW - Bayesian networks, directed acyclic graph, Arthur Cayley, intervention calculus, graphical Markov model, Markov equivalence, structure learning

UR - http://eudml.org/doc/292706

ER -

## NotesEmbed ?

topTo embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.