An empirical study of testing independencies in Bayesian networks using rp-separation

Butz, Cory J.; Santos, André E. dos; Oliveira, Jéssica de; Gonzales, Christophe

doi:10.1016/j.ijar.2017.10.026

Cited by 2 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DNs have led to a faster approach for testing independencies in BNs, called i‐separation (Butz et al ).…”

Section: Practical Applications Of Darwinian Networkmentioning

confidence: 99%

“…We proposed inaugural‐separation ( i‐separation ) in (Butz et al ) as a novel method for testing independencies in BNs. We introduced the notion of an inaugural (Butz et al ) variable, the salient feature of which is that in testing I ( X , Y , Z ), any path from X to Z involving an inaugural variable is blocked. This means that paths involving inaugural variables can be safely ignored and pruned from the BN.…”

Section: Practical Applications Of Darwinian Networkmentioning

confidence: 99%

“…Table (Butz et al ) reports an empirical comparison between d‐separation and i‐separation. The experiments were performed on the 18 benchmark BNs listed in column 1.…”

Section: Practical Applications Of Darwinian Networkmentioning

confidence: 99%

See 2 more Smart Citations

On Darwinian Networks

Butz

Oliveira

Santos

2017

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

We suggest Darwinian Networks (DNs) as a simplification of working with Bayesian networks (BNs). DNs adapt a handful of well‐known concepts in biology into a single framework that is surprisingly simple yet remarkably robust. With respect to modeling, on one hand, DNs not only represent BNs but also faithfully represent the testing of independencies in a more straightforward fashion. On the other hand, with respect to three exact inference algorithms in BNs, DNs simplify each of them while unifying all of them. DNs can determine good elimination orderings using the same platform as used for modeling and inference. Finally, we demonstrate how DNs can represent two additional frameworks. Practical benefits of DNs include faster algorithms for inference and modeling.

show abstract

“…DNs have led to a faster approach for testing independencies in BNs, called i‐separation (Butz et al ).…”

Section: Practical Applications Of Darwinian Networkmentioning

confidence: 99%

Section: Practical Applications Of Darwinian Networkmentioning

confidence: 99%

See 1 more Smart Citation

On Darwinian Networks

Butz

Oliveira

Santos

2017

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…A variable v k is called a v-structure 14 Given an independence I(X, Y, Z) to be tested in a BN , a variable v is inaugural, 8 if at least 1 of the following 2 conditions is satisfied: (1) v is a v-structure and…”

Section: U-separationmentioning

confidence: 99%

“…7 Moreover, d-separation may necessarily consult descendants of some of the variables on the path being checked. We refer the reader to Butz et al 8 for further discussion of these issues.…”

mentioning

confidence: 99%

On a simple method for testing independencies in Bayesian networks

Butz

Santos

Oliveira

et al. 2017

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Testing independencies is a fundamental task in reasoning with Bayesian networks (BNs). In practice, d-separation is often used for this task, since it has linear-time complexity. However, many have had difficulties understanding d-separation in BNs. An equivalent method that is easier to understand, called m-separation, transforms the problem from directed separation in BNs into classical separation in undirected graphs. Two main steps of this transformation are pruning the BN and adding undirected edges. In this paper, we propose u-separation as an even simpler method for testing independencies in a BN. Our approach also converts the problem into classical separation in an undirected graph. However, our method is based upon the novel concepts of inaugural variables and rationalization. Thereby, the primary advantage of u-separation over m-separation is that m-separation can prune unnecessarily and add superfluous edges. Our experiment results show that u-separation performs 73% fewer modifications on average than m-separation.Pearl 1 states that perhaps the founding of Bayesian networks (BNs) 2-4 made its greatest impact through the notion of d-separation. d-Separation 5 is a graphical method for deciding which conditional independence relations are implied by the directed acyclic graph (DAG) of a BN. To test whether 2 sets X and Z of variables are conditionally independent given a third set Y of variables, denoted I(X, Y, Z), d-separation checks whether every path from X to Z is "blocked" by Y in the DAG. d-Separation Computational Intelligence. 2018;34:789-801.wileyonlinelibrary.com/journal/coin

show abstract

An empirical study of testing independencies in Bayesian networks using rp-separation

Cited by 2 publications

References 11 publications

On Darwinian Networks

On Darwinian Networks

On a simple method for testing independencies in Bayesian networks

Contact Info

Product

Resources

About