A Stratified Simulation Scheme for Inference in Bayesian Belief Networks

Bouckaert, Remco R.

doi:10.1016/b978-1-55860-332-5.50019-5

Cited by 28 publications

(36 citation statements)

References 11 publications

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“…Examples include those of Bouckaert (1994c), Bouckaert et al (1996) and Cano et al (1996) who look at ways to more evenly sample the space. Examples include those of Bouckaert (1994c), Bouckaert et al (1996) and Cano et al (1996) who look at ways to more evenly sample the space.…”

Section: Logic Samplingmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

269

194

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Daly, R., Shen, Q., Aitken, S. (2011). Learning Bayesian networks: approaches and issues. Knowledge Engineering Review, 26 (2), 99-157Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks?in particular their structure?from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial?for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.Peer reviewe

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Section: Logic Samplingmentioning

confidence: 99%

Learning Bayesian networks: approaches and issues

Daly

Shen

Aitken

2011

The Knowledge Engineering Review

269

194

View full text Add to dashboard Cite

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“…. , X σ (1) . To obtain a value for a variable X σ(i) , we sample from the function h i , making the restriction of it to the values x 0 already obtained for the preceding variables, X Σ(i) , and normalizing afterwards.…”

Section: Proposition 1 Assume the Conditions For Exact Deletionmentioning

confidence: 99%

“…First propagation algorithms based on stratified sampling were proposed by Bouckaert [1] and Bouckaert, Castillo and Gutiérrez [2]. The idea is to consider the space of all possible configurations of the variables in the network, so that most probable configurations are assigned to a greater region.…”

Section: Stratified Sampling In Belief Networkmentioning

confidence: 99%

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A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques

Hernández¹,

Moral²,

Salmerón³

1998

International Journal of Approximate Reasoning

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A class of Monte Carlo algorithms for probability propagation in belief networks is given.The simulation is based on a two steps procedure. The first one is a node deletion technique to calculate the 'a posteriori' distribution on a variable, with the particularity that when exact computations are too costly, they are carried out in an approximate way. In the second step, the computations done in the first one are used to obtain random configurations for the variables of interest. These configurations are weighted according to the importance sampling methodology. Different particular algorithms are obtained depending on the approximation procedure used in the first step and in the way of obtaining the random configurations. In this last case, a stratified sampling technique is used, which has been adapted to be applied to very large networks without problems with round-off errors.

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“…For this reason, these classification domains seem to be suitable for the application of feature selection methods. These methods are centered in obtaining a subset of features that adequately describe the problem at hand without degrading (and most of the times, improving) performance [6]. Feature selection is primarily performed to select relevant informative features, but it can have other motivations, including general data reduction, feature set reduction, performance improvement and better data understanding.…”

Section: Introductionmentioning

confidence: 99%