Repeated patterns is a useful model specification short cut for Bayesian networks

1 | ```
repeatPattern(plist, instances, unlist = TRUE)
``` |

`plist` |
A list of conditional probability tables. The variable
names must have the form |

`instances` |
A vector of distinct integers |

`unlist` |
If |

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. http://www.jstatsoft.org/v46/i10/.

`grain`

, `compileCPT`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## Specify hidden markov models. The x[i]'s are unobserved, the
## y[i]'s can be observed.
yn <- c("yes","no")
## Specify p(x0)
x.0 <- cptable(~x0, values=c(1,1), levels=yn)
## Specify transition density
x.x <- cptable(~x[i]|x[i-1], values=c(1,99,2,98),levels=yn)
## Specify emissiob density
y.x <- cptable(~y[i]|x[i], values=c(1,99,2,98),levels=yn)
## The pattern to be repeated
pp <- list(x.x, y.x)
## Repeat pattern and create network
ppp <- repeatPattern(pp, instances=1:10)
qqq <- compileCPT(c(list(x.0),ppp))
rrr <- grain(qqq)
``` |

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