[R] Random seed problem in MCMC coupling of chains

[Paul Gilbert]

Beware that your easy trick will give you the same result every time you 
run it. You need a better scheme if you actually intend to get a new 
experiment each time you run it.

Paul

Gorjanc Gregor wrote:

> Thanks to Duncan, Dimitris as well as James for answers. I'll provide
> here also example from James, which seems to be the easiest of them 
> all and was not posted to the list:
> 
> niter <- 3
> nchain <- 2
> for (i in 1:niter) { # iterations
>   for (j in 1:nchain) { # chains
>     set.seed(i)
>     a <- runif(1)
>     cat("iter:", i, "chain:", j, "runif:", a, "\n")
>   }
> }
> 
> Note that seed is set with iteration counter. This is really neat and
> simple. I am just wondering if this is OK from "RNG point of view". Can
> someone comment on that?
> 
> Lep pozdrav / With regards,
>     Gregor Gorjanc
> 
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> 
> -----Original Message-----
> From: Duncan Murdoch [mailto:murdoch at stats.uwo.ca]
> Sent: sre 2005-06-08 15:53
> To: Gorjanc Gregor
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] Random seed problem in MCMC coupling of chains
>  
> On 6/8/2005 9:27 AM, Gorjanc Gregor wrote:
> 
>>Hello!
>>
>>I am performing coupling of chains in MCMC and I need the same value
>>of seed for two chains. I will show demo of what I want:
>>
>>R code, which might show my example is:
>>niter <- 3
>>nchain <- 2
>>tmpSeed <- 123
>>for (i in 1:niter) { # iterations
>>  for (j in 1:nchain) { # chains
>>    set.seed(tmpSeed)
>>    a <- runif(1)
>>    cat("iter:", i, "chain:", j, "runif:", a, "\n")
>>    tmpSeed <- .Random.seed
>>  }
>>}
>>
>>I get this:
>>
>>iter: 1 chain: 1 runif: 0.43588
>>iter: 1 chain: 2 runif: 0.43588
>>iter: 2 chain: 1 runif: 0.43588
>>iter: 2 chain: 2 runif: 0.43588
>>iter: 3 chain: 1 runif: 0.43588
>>iter: 3 chain: 2 runif: 0.43588
>>
>>but I would like to get:
>>
>>iter: 1 chain: 1 runif: 0.43588
>>iter: 1 chain: 2 runif: 0.43588
>>iter: 2 chain: 1 runif: 0.67676
>>iter: 2 chain: 2 runif: 0.67676
>>iter: 3 chain: 1 runif: 0.12368
>>iter: 3 chain: 2 runif: 0.12368
>>
>>Note that seed value is of course changing, but it is parallel
>>between chains.
> 
> 
> set.seed takes an integer, but .Random.seed is a complicated vector. 
> You need to play with .Random.seed directly, and move your setting of 
> tmpSeed out of the inner loop, i.e.
> 
>  > niter <- 3
>  > nchain <- 2
>  > set.seed(123)
>  > tmpSeed <- .Random.seed
>  > for (i in 1:niter) { # iterations
> +   for (j in 1:nchain) { # chains
> +     .Random.seed <- tmpSeed
> +     a <- runif(1)
> +     cat("iter:", i, "chain:", j, "runif:", a, "\n")
> +   }
> +   tmpSeed <- .Random.seed
> + }
> iter: 1 chain: 1 runif: 0.2875775
> iter: 1 chain: 2 runif: 0.2875775
> iter: 2 chain: 1 runif: 0.7883051
> iter: 2 chain: 2 runif: 0.7883051
> iter: 3 chain: 1 runif: 0.4089769
> iter: 3 chain: 2 runif: 0.4089769
> 
> However, heed the warnings in ?set.seed:  with some generators 
> .Random.seed *does not* contain the full state of the RNG.  As far as I 
> know there is no way to obtain the full state.
> 
> Duncan Murdoch
> 
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