```
## 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))
```

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