cpquery: Perform conditional probability queries
In jtrecenti/bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Description Usage Arguments Details Value Logic Sampling Likelihood Weighting Author(s) References Examples

Perform conditional probability queries (CPQs).

cpquery(fitted, event, evidence, cluster = NULL, method = "ls", ...,
  debug = FALSE)
cpdist(fitted, nodes, evidence, cluster = NULL, method = "ls", ...,
  debug = FALSE)

mutilated(x, evidence)

`fitted`	an object of class `bn.fit`.
`x`	an object of class `bn` or `bn.fit`.
`event, evidence`	see below.
`nodes`	a vector of character strings, the labels of the nodes whose conditional distribution we are interested in.
`cluster`	an optional cluster object from package parallel. See `parallel integration` for details and a simple example.
`method`	a character string, the method used to perform the conditional probability query. Currently only logic sampling (`ls`, the default) and likelihood weighting (`lw`) are implemented.
`...`	additional tuning parameters.
`debug`	a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

cpquery estimates the conditional probability of event given evidence using the method specified in the method argument.

cpdist generates random observations conditional on the evidence using the method specified in the method argument.

mutilated constructs the mutilated network used for sampling in likelihood weighting.

cpquery returns a numeric value, the conditional probability of event conditional on evidence.

cpudist returns a data frame containing the observations generated from the conditional distribution of the nodes conditional on evidence. The data frame has class c("bn.cpdist", "data.frame"), and a method attribute storing the value of the method argument. In the case of likelihood weighting, the weights are also attached as an attribute called weights.

mutilated returns a bn or bn.fit object, depending on the class of x.

The event and evidence arguments must be two expressions describing the event of interest and the conditioning evidence in a format such that, if we denote with data the data set the network was learned from, data[evidence, ] and data[event, ] return the correct observations. If either event or evidence is set to TRUE an unconditional probability query is performed with respect to that argument.

Three tuning parameters are available:

n: a positive integer number, the number of random observations to generate from fitted. Defaults to 5000 * log10(nparams.fitted(fitted)) for discrete networks and
500 * nparams.fitted(fitted) for Gaussian networks.
batch: a positive integer number, the size of each batch of random observations. Defaults to 10^4.
query.nodes: a a vector of character strings, the labels of the nodes involved in event and evidence. Simple queries do not require to generate observations from all the nodes in the network, so cpquery and cpdist try to identify which nodes are used in event and evidence and reduce the network to their upper closure. query.nodes may be used to manually specify these nodes when automatic identification fails; there is no reason to use it otherwise.

Note that the number of observations returned by cpdist is always smaller than n, because logic sampling is a form of rejection sampling. Therefore, only the obervations matching evidence (out of the n that are generated) are returned, and their number depends on the probability of evidence.

The event argument must be an expression describing the event of interest, as in logic sampling. The evidence argument must be a named list:

Each element corresponds to one node in the network and must contain the value that node will be set to when sampling.
In the case of a continuous node, two values can also be provided. In that case, the value for that node will be sampled from a uniform distribution on the interval delimited by the specified values.
In the case of a discrete or ordinal node, two or more values can also be provided. In that case, the value for that node will be sampled with uniform probability from the set of specified values.

If either event or evidence is set to TRUE an unconditional probability query is performed with respect to that argument.

Tuning parameters are the same as for logic sampling: n, batch and query.nodes.

Note that the observations returned by cpdist are generated from the mutilated network, and need to be weighted appropriately when computing summary statistics (for more details, see the references below). cpquery does that automatically when computing the final conditional probability. Also note that the batch argument is ignored in cpdist for speed and memory efficiency.

Marco Scutari

Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.

Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.

## discrete Bayesian network (it is the same with ordinal nodes).
data(learning.test)
fitted = bn.fit(hc(learning.test), learning.test)
# the result should be around 0.025.
cpquery(fitted, (B == "b"), (A == "a"))
# for a single observation, predict the value of a single
# variable conditional on the others.
var = names(learning.test)
obs = 2
str = paste("(", names(learning.test)[-3], "=='",
        sapply(learning.test[obs,-3], as.character), "')",
        sep = "", collapse = " & ")
str
str2 = paste("(", names(learning.test)[3], "=='",
         as.character(learning.test[obs, 3]), "')", sep = "")
str2
cpquery(fitted, eval(parse(text = str2)), eval(parse(text = str)))
# do the same with likelihood weighting
cpquery(fitted, event = eval(parse(text = str2)),
  evidence = as.list(learning.test[2, -3]), method = "lw")
# conditional distribution of A given C == "c".
table(cpdist(fitted, "A", (C == "c")))

## Gaussian Bayesian network.
data(gaussian.test)
fitted = bn.fit(hc(gaussian.test), gaussian.test)
# the result should be around 0.04.
cpquery(fitted,
  event = ((A >= 0) & (A <= 1)) & ((B >= 0) & (B <= 3)),
  evidence = (C + D < 10))