old_replace_cpt | R Documentation |
Replace CPTs of Bayesian network.
replaceCPT(object, value)
object |
A |
value |
A named list, see examples below. |
When a Bayesian network (BN) is constructed from a list of
conditional probability tables (CPTs) (e.g. using the function
grain()
), various actions are taken:
It is checked that the list of CPTs define a directed acyclic graph (DAG).
The DAG is moralized and triangulated.
A list of clique potentials (one for each clique in the triangulated graph) is created from the list of CPTs.
The clique potentials are, by default, calibrated to each other so that the potentials contain marginal distributions.
The function described here bypass the first two steps which can provide an important gain in speed compared to constructing a new BN with a new set of CPTs with the same DAG.
Søren Højsgaard, sorenh@math.aau.dk
Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. https://www.jstatsoft.org/v46/i10/.
grain
, propagate
,
triangulate
, rip
,
junctionTree
## See the wet grass example at
## https://en.wikipedia.org/wiki/Bayesian_network
yn <- c("yes", "no")
p.R <- cptable(~R, values=c(.2, .8), levels=yn)
p.S_R <- cptable(~S:R, values=c(.01, .99, .4, .6), levels=yn)
p.G_SR <- cptable(~G:S:R, values=c(.99, .01, .8, .2, .9, .1, 0, 1), levels=yn)
wet.bn <- compileCPT(p.R, p.S_R, p.G_SR) |> grain()
getgrain(wet.bn, "cpt")[c("R","S")]
# Update some CPTs
wet.bn <- replace_cpt(wet.bn, list(R=c(.3, .7), S=c(.1, .9, .7, .3)))
getgrain(wet.bn, "cpt")[c("R","S")]
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