I have a multivariate Monte-Carlo Hidden Markov problem to solve: x[k] = f(x[k-1]) + B u[k] y[k] = g(x[k]) where: x[k] the hidden states (Markov dynamics) y[k] the observed data u[k] the stochastic driving process Is PyMC3 already mature enough to handle this problem or should I stay with version 2.3? Secondly, any references to HM models in a PyMC framework would be much appreciated. Thanks. -- H
Could someone give some general instructions on how one can parallelize the PyMC MCMC code. I am trying to run LASSO regression following the example given here. I read somewhere that parallel sampling is done by default, but do I still need to use something like Parallel Python to get it to work? Here is some reference code that I would like to be able to parallelize on my machine. x1 = norm.rvs(
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