Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Heckerman, David; Geiger, Dan; Chickering, David Maxwell

doi:10.1016/b978-1-55860-332-5.50042-0

Cited by 1,085 publications

(1,552 citation statements)

References 3 publications

Supporting

Mentioning

1,517

Contrasting

Unclassified

Order By: Relevance

“…Although the origin of Bayesian Networks modeling is related to the field of computer science (e.g. machine learning and data mining) (Heckerman et al, 1995;Needham et al, 2007), these models have also been used recently in health sciences and epidemiology (Lewis and McCormick, 2012;Lewis and Ward, 2013;Caillet et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

Álvarez‐Gálvez

2016

Social Science Research

View full text Add to dashboard Cite

a b s t r a c tStudies assume that socioeconomic status determines individuals' states of health, but how does health determine socioeconomic status? And how does this association vary depending on contextual differences? To answer this question, our study uses an additive Bayesian Networks model to explain the interrelationships between health and socioeconomic determinants using complex and messy data. This model has been used to find the most probable structure in a network to describe the interdependence of these factors in five European welfare state regimes. The advantage of this study is that it offers a specific picture to describe the complex interrelationship between socioeconomic determinants and health, producing a network that is controlled by socio-demographic factors such as gender and age. The present work provides a general framework to describe and understand the complex association between socioeconomic determinants and health.

show abstract

Section: Introductionmentioning

confidence: 99%

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

Álvarez‐Gálvez

2016

Social Science Research

View full text Add to dashboard Cite

show abstract

“…We speculate that the sparsity of DBN learning approaches applied to spike trains is due to the scoring functions that are commonly used (e.g. the Bayesian Dirichlet (BD) score (Cooper and Herskovits 1992;Heckerman et al 1995), Bayesian Dirichlet equivalence (BDe) score (Heckerman et al 1995), or the minimal description length (MDL) (Lam and Bacchus 1994), which is equivalent (Friedman 1997) to the Bayesian information criterion (BIC) (Schwarz 1978)). These scores are not specifically adapted to any data-type, i.e.…”

Section: Resultsmentioning

confidence: 99%

Causal pattern recovery from neural spike train data using the Snap Shot Score

2009

View full text Add to dashboard Cite

We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

show abstract

“…The values of θ ijk are estimated by the number of corresponding co-occurrences N ijk throughout the time-series based on the maximum likelihood criterion. Bayesian Dirichlet metric is used for the assignment of α ijk and the total likelihood can be calculated by (Heckerman et al, 1995):…”

Section: Structure Learning Of Dbnmentioning

confidence: 99%