artificial-intelligence,bayesian,bayesian-networks

You first of all apply the Bayes rule and you get: P(A|B) = P(A) * P(B|A) / P(B) then you sum over the values of a to get: P(A|B) = P(A) * P(B|A) / (sum_{a} P(B,a)) and then you apply the chain rule: P(A|B) = P(A) * P(B|A) / (sum_{a}...

machine-learning,pattern-recognition,bayesian-networks

Is this the right approach? There's many possible approaches, but here's a very simple and effective one that fits the domain: Given the nature of the application, chronological order doesn't really matter, it doesn't matter if the Fan gets turned on before the Light e.g. Also given that you...

javascript,probability,bayesian-networks

The following seems to do the job up to marginalise. It creates a table with rows that have members and a value. Each row in a table must have the same number of members. It doesn't implement multiplication yet (real work has to be done sometime…). Note that in ECMAScript,...

machine-learning,neural-network,bayesian-networks,supervised-learning

There can be no absolute "A is better than B" answer to a such question. The performance of each system depends how the data are situated in the problem space, and in different problems some algorithms are more suitable than others. That being said, if you want to select a...

Try getProbability method on BayesNet class. Here is what I do. for(int i = 0; i < bnet.getCardinality(nodeIndex); i++) { System.out.print(bnet.getNodeValue(nodeIndex, i) + " = " + bnet.getProbability(nodeIndex, row, i) + " "); } Where row is 0 <= row < bnet.getParentCardinality() and each value of row corresponds to a...

r,null,nan,bayesian,bayesian-networks

the catnet package can deal with missing data and is designed for discrete data you can get conditional probabilities using cnProb(bnet,which) here is an example rm(list=ls()) ### generate random data (not simply independent binomials) set.seed(123) n.obs <- 10 a1 <- rbinom(n.obs,1,.3) a2 <- runif(n.obs) a3 <- floor(-3*log(.25+3*a2/4)) a3[a3>=2] <- NA...

bayesian,bayesian-networks,probability-theory

It's easiest to maintain the joint probability table, and rebuild the CPT from that as needed. Along with the JPT, keep a count of how many examples were used to produce it. When adding the nth example, multiply all probabilities by 1 - 1/n, and then add probability 1/n to...

The packagesgraph, RBGL and Rgraphviz packages are not on CRAN but on bioconductor. To install these packages, execute source("http://bioconductor.org/biocLite.R") biocLite(c("graph", "RBGL", "Rgraphviz")) Then install the packages from CRAN in the usual way: install.packages("gRain", dependencies=TRUE) See also the grain installation instructions....