Adoption of MCMC methods

Markov Chain Monte Carlo methods suffer from several issues among which:

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
)

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