CosmoCoffee

Dear All,

I am testing a model with exponential parametrization of the reionization and I am facing the problem to run chains on cosmomc with a parameter (the exponent) which has a one tail distribution.

Do you have any advice to help the convergence of the Monte-Carlo? I would like to test a few decades of this parameter, but the distribution becomes flat quickly and it is taking forever to reach convergence.


Thanks a lot,
Daniel

Re-parameterize, e.g. using the log of that variable as the MCMC parameter? (though note that this also changes the implicit flat prior).

Indeed, the log would work, but since the parametrization chosen has some physical meaning, I would have to fix the prior to transform for a flat prior on the original parameter.


Thanks Antony,
Daniel

Dear Antony,

To fix the prior, would you sugest to use the function AdjustPrior(), on GetDist?

Moreover, is it still enough to process the chains only using these lines below?

! coldata(1,i) = coldata(1,i)*exp(-chisq/2)
! coldata(2,i) = coldata(2,i) + chisq/2


Thanks,
Daniel

I think that should work. (you may also want to adjust the chi2_xxx derived parameters, if you use them for anything)

Ok, I am only interested in the total chi2 anyway. I was concerned that these new chi2_xxx derived parameters had changed the meaning of the first two columns, making the AdjustPrior() deprecated.

Thanks for explaining.


Cheers,