At present, I have developed my before code to more patches. It has initialization failure problem. I think it is because I am using exponential for my likelihood function. The value are very small, so it might underflow. I know one way ‘log-sum-exp’ trick. Each likelihood minus the maximum likelihood and get the new likelihood.
So anyone can provide me an example how stan use that? Thanks.
Stan has a log_sum_exp function. The mixture chapter of the Stan book describes what the function is doing.
Thanks, I’ll try that!
Hi,
I think the log_sum _ exp function is doing
log(a+b) = log \underline sum \underline exp(log(a),log(b)).
So for mixture model, it can be written in log.sum.exp(log(lambda)+normal_log(y,mu[1],sigma[1]),loglm(lambda)+normal_log(y,mu[2],sigma[2])).
But for my problem, my likelihood is a exponential distribution. The likelihood is like below:
f(y)\sim mixture\space gaussian(\theta)
Likelihood(y,c|\theta) = \prod \space exp((1/f(y))*Ci)
So my loglikelihood would be
\sum log(lamda[i])-lamda[i]*Ci
How could I forbid the underflow for that kind of likelihood function? Thanks.