Hi
I have a situation with fitting parameters for a large model. One sample model can take days to run. The implication is few samples.
For predictions I can use Latin Hyper Cube (LHP) sampling and improve on the statistical results for especially fractions like P10/P90. The mean converges OK.
I guess the LHP sampling breaks some basic assumptions in Bayesian updating scheme. But then has anyone attempted alternative sampling methods like LHC for Bayesian updating?
Have you considered Sequential Monte Carlo (which has implementations in PyMC)? I assume that you are using LHC to speed up sampling by handling each dimension in your parameter space independently. I think SMC does the same.
Separately, I am curious to learn how you are fitting LHC into the Bayesian framework if you are willing to share more about your project.
The thing is that I do not use LHC for parameter estimation, as it’s dubious in relation to Bayesian updating. The thinking is that I would cover the parameter space more efficiently.
The question is whether this is a futile approach in the Bayesian context. My thinking is that this group have some really strong people. Someone must have tried this before.
I dug this old thread up where the OP includes code that appears to use LHC sampling. Maybe that provides some directions?