[R] Random seed problem in MCMC coupling of chains

[Gabor Grothendieck]

Here is a small variation.   We define a list to hold
the last seed for each chain.  Each time we enter the simulation
for a chain we use that seed and each time we exit we update it.
The loop becomes simpler since the setup is all done prior
to looping and everything else is done in the inner loop.

Note that a double loop with nothing between the first and second
for is really like a single loop over the i,j pairs so its presumably
easier to understand.  

library(setRNG)

set.seed(123)
niter <- 3; nchain <- 2
chain <- lapply(1:nchain, function(x) setRNG())

for(i in 1:niter)
  for(j in 1:nchain) {  

    setRNG(chain[[j]])   # get seed

    a <- runif(1)
    cat("iter:", i, "chain:", j, "runif:", a, "\n")

    chain[[j]] <- setRNG()  # save seed

  }

On 6/8/05, Gorjanc Gregor <Gregor.Gorjanc at bfro.uni-lj.si> wrote:
> Thanks to Paul and Gabor for additional tips/examples. Actually, I find
> Pauls suggestion with setRNG also nice and is exactly what I wanted.
> Paul, if I understand this correctly, your suggestion with setRNG does not
> alter "RNG flow", it just takes care that chains really have equal seeds.
> I remember that I have read somewhere that destroying "RNG flow over and
> over to get real randomness" is not a good idea. Can someone confirm this?
> 
> niter <- 3
> nchain <- 2
> for (i in 1:niter) { # iterations
>  tmpSeed <- setRNG()
>  for (j in 1:nchain) { # chains
>    setRNG(tmpSeed)
>    a <- runif(1)
>    cat("iter:", i, "chain:", j, "runif:", a, "\n")
>  }
> }
> 
> iter: 1 chain: 1 runif: 0.8160078
> iter: 1 chain: 2 runif: 0.8160078
> iter: 2 chain: 1 runif: 0.4909793
> iter: 2 chain: 2 runif: 0.4909793
> iter: 3 chain: 1 runif: 0.4425924
> iter: 3 chain: 2 runif: 0.4425924
> 
> [... removed other stuff ...]
> 
> Lep pozdrav / With regards,
>    Gregor Gorjanc
> 
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