Bayesian Networks

José P. González-Brenes; John T. Behrens; Robert J. Mislevy; Roy Levy; Kristen E. Dicerbo

doi:10.1002/9781118956588.ch14

Bayesian Networks

José P. González-Brenes, John T. Behrens, Robert J. Mislevy, Roy Levy, Kristen E. Dicerbo

Research output: Chapter in Book/Report/Conference proceeding › Chapter

5 Scopus citations

Abstract

Bayesian Networks or Bayes Nets (BNs) are a general-purpose computational and statistical framework. BNs allow modeling a broad range of phenomena by reasoning about collected evidence and by updating beliefs in light of new data. In the context of supporting assessment, BNs are interesting because they align with the perspective of evidence-centered assessment design. In this chapter, we discuss how BNs can be used to formalize substantively grounded reasoning processes, we describe the statistical formalism of BNs through some core equations, we illustrate the flexibility of BNs by providing various extensions in simple graphical representations, and we provide examples for modeling cognition across educational, psychological, and linguistic contexts.

Original language	English (US)
Title of host publication	The Handbook of Cognition and Assessment
Publisher	Wiley-Blackwell
Pages	328-353
Number of pages	26
ISBN (Electronic)	9781118956588
ISBN (Print)	9781118956571
DOIs	https://doi.org/10.1002/9781118956588.ch14
State	Published - Sep 22 2016

Keywords

Bayesian networks
Cognitive models
Evidence centered design
Probability

ASJC Scopus subject areas

General Social Sciences

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10.1002/9781118956588.ch14

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title = "Bayesian Networks",

abstract = "Bayesian Networks or Bayes Nets (BNs) are a general-purpose computational and statistical framework. BNs allow modeling a broad range of phenomena by reasoning about collected evidence and by updating beliefs in light of new data. In the context of supporting assessment, BNs are interesting because they align with the perspective of evidence-centered assessment design. In this chapter, we discuss how BNs can be used to formalize substantively grounded reasoning processes, we describe the statistical formalism of BNs through some core equations, we illustrate the flexibility of BNs by providing various extensions in simple graphical representations, and we provide examples for modeling cognition across educational, psychological, and linguistic contexts.",

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AU - González-Brenes, José P.

AU - Behrens, John T.

AU - Mislevy, Robert J.

AU - Levy, Roy

AU - Dicerbo, Kristen E.

PY - 2016/9/22

Y1 - 2016/9/22

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AB - Bayesian Networks or Bayes Nets (BNs) are a general-purpose computational and statistical framework. BNs allow modeling a broad range of phenomena by reasoning about collected evidence and by updating beliefs in light of new data. In the context of supporting assessment, BNs are interesting because they align with the perspective of evidence-centered assessment design. In this chapter, we discuss how BNs can be used to formalize substantively grounded reasoning processes, we describe the statistical formalism of BNs through some core equations, we illustrate the flexibility of BNs by providing various extensions in simple graphical representations, and we provide examples for modeling cognition across educational, psychological, and linguistic contexts.

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KW - Cognitive models

KW - Evidence centered design

KW - Probability

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BT - The Handbook of Cognition and Assessment

PB - Wiley-Blackwell

ER -

Bayesian Networks

Abstract

Keywords

ASJC Scopus subject areas

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