Hi, I’m trying to use Pymc3 to find the appropriate number of componements used to predict a time serie of concentrations called aCH and aCOH. These are two distincts PLS models. Each model can use between 1 and 20 number of components. The predictions are stored in an ndarray with the time series …

Yes using Metropolis could produce invalid proposal as described in the other post you mentioned, but using Categorical distribution seems to work: n = aCH_.eval().shape[1] with pm.Model() as basic_model: # Priors for unknown model parameters b1 = pm.Uniform('b1', lower=0.3, upper=0.5, test…

Could you share your data as well?

Yes sure, here are the data aCOH_, aCH_ , Y and sigma. These are numpy objects. aCH.npy (18.7 KB) aCOH.npy (18.7 KB)

In two different posts because I’m not allowed to put more than two attachements. sigma.npy (1.1 KB) Y.npy (1.1 KB)

Thanks, yes it works! But there is no way to make it work using Metropolis?

I have run this model with 8 millions iterations with 1 millions tuning steps and still have a Gelman Rubin above 1.4 and a lot of divergences. Do you know have any idea of what causes that problem? Increasing target_accept doesn’t help either. [Capture]

More iterations doesn’t help when there are problems in your model. In this case, a few suggestion: Increase target_accept leads to smaller step size, but it only helps when you are seeing <10 divergences. When there are lots of divergent, it means something is wrong with the model looking at the …

Thanks for the advices! I’m going to try to mix Categorical GibbsSampler with Metropolis instead of Nuts as these sampling methods are closer. Concerning the b1 coefficient, it really should be a scaler and not a vector. It is a physical property that should be the same for each sample.

If I use Metropolis, then the variables updated by Categorical Gibbs Sampler are not updated at all. This is very strange. Do you have any idea why?

Indexing using a free random variable

Questions

junpenglao March 13, 2018, 12:44pm 7

Currently, Metropolis still suffer the same problem as explained in Issue with DiscreteUniform distribution…

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Indexing using a free random variable

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