Adoption of MCMC methods
Markov Chain Monte Carlo methods suffer from several issues among which:
- Getting MCMC to work out of the box is hard;
- Current frameworks are not built for production;
- Hard to express models in a PPL-friendly way;
- No easy-to-use software to cover most common use cases (Bambi);
It would be rather nice to have a tool like
import aesara.tensor as at import aemcmc import daeploy srng = at.random.RandomStream(0) mu_rv = srng.normal(0, 1) s_rv = srng.halfnormal(1.) Y_t = srng.normal(0, s_rv) # samples locally results = aemcmc.sample( {Y_t: data}, num_warmup=1000, num_samples=1000 ) # samples automatically, on the cloud results = deploy.sample( {Y_t: data}, num_warmup=1000, num_samples=1000 )