# repeatPattern: Create repeated patterns in Bayesian networks In gRain: Graphical Independence Networks

## Description

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

## Usage

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

## Arguments

 `plist` A list of conditional probability tables. The variable names must have the form `name[i]` and the `i` will be substituted by the values given in `instances` below. `instances` A vector of distinct integers `unlist` If `FALSE` the result is a list in which each element is a copy of `plist` in which `name[i]` are substituted. If `TRUE` the result is the result of applying `unlist()`.

## Author(s)

S<c3><b8>ren H<c3><b8>jsgaard, [email protected]

## References

S<c3><b8>ren H<c3><b8>jsgaard (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) ```