Do not compute the log likelihood as the log of the product of
probabilities. Instead compute it as the sum of logs of probabilities.
The latter is less likely to underflow (go below c. 0^-309).
Most (all?) of the built-in probability density functions have a 'log'
argument; when log=TRUE you get
Hello list,
I' trying to estimate a log likelihood function from my data.
I apply the mean to all my simulations, and I get something like this:
apply(likelihood, c(2, 3, 4), mean,na.rm=TRUE)
, , 1
[,1] [,2] [,3] [,4] [,5][,6]
[,7]
[1,] 0.73162327 0.
2 matches
Mail list logo
