Description Usage Arguments Examples
Computes the most useful intervention according to information, probability and utility gain. Requires a dataframe called li in the global workspace that encodes the likelihoods of any outcome of any intervention created with likelihood() Requires a dataframe called o which incodes the outcome space (see example) Requires a matrix of interventions called ints, 1 is on, 0 is free, -1 is off.
1 | choose_int(g, pG, compute_value = 1)
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g |
matrix of hypothesis graphs, one line is one graph to be written by row to a matrix. |
compute_value |
defaults to 0=no. If 1 a value matrix C must be provided with a value for each judgement i given each true network j Additional arguments can then be passed to this objective function |
pDist |
is the prior distribution over these graphs. |
1 2 3 4 5 6 | ints<-as.matrix(expand.grid(rep(list(0:2), 3)))
ints[ints==2]<--1
o<-expand.grid(rep(list(0:1 ), sqrt(dim(g)[2])))
dist<-prior('flat',g)
li<-likelihood(g,.1,.8)
out<-choose_int(g, dist, compute_value=0)
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