Duncan Murdoch <murdoch.duncan <at> gmail.com> writes: > > On 11-10-18 4:30 AM, Seref Arikan wrote: > > Hi Dan, > > I've tried the log likelihood, but it reaches zero again, if I work with say > > 1000 samples. > > I need an approach that would scale to quite large sample sizes. Surely I > > can't be the first one to encounter this problem, and I'm sure I'm missing > > an option that is embarrassingly obvious. > > I think you are calculating the log likelihood incorrectly. Don't > calculate the likelihood and take the log; work out the formula for the > log of the likelihood, and calculate that. (If the likelihood contains > a sum of terms, as in a mixture model, this takes some thinking, but it > is still worthwhile.) > > With most models, it is just about impossible to cause the log > likelihood to underflow if it is calculated carefully. > > Duncan Murdoch >
I haven't followed this carefully, but there is a special problem in Bayesian situations where at some point you may have to *integrate* the likelihoods, which implies dealing with them on the likelihood (not log-likelihood) scale. There are various ways of dealing with this, but one is to factor a constant (which will be a very small value) out of the elements in the integrand. Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
