Hi, I’m toying with a Normal MixtureModel. The weights are Dirichlet: import pymc3 as pm import numpy as np import theano.tensor as tt W = np.array([0.35, 0.4, 0.25]) with pm.Model() as model: w = pm.Dirichlet('w', np.ones_like(W)) mu = pm.Normal('mu', 0., 10., shape=W.size) tau = pm.Gamma('ta…

We do not allow dynamic shape in model, so currently this is impossible to do. One potential work around is having a fix shape alpha with a shape is equal to the max shape (5 in this case), and dynamically assign zeros to some element. But again, inferencing these kind of model is also very tricky. …

Thanks for the response. Following your suggestion to dynamically assign zeros I have this attempt: with pm.Model() as model: k = 5 arr = [] for j in range(k): arr.append(pm.Uniform('a'+str(j),lower=0,upper=0.99)) w = pm.Dirichlet('w', a=tt.stack([arr[i] for i in range(k)]), shape=k) du = …

I have something that seemingly answers my previous question: with pm.Model() as model: k = 5 w = pm.Dirichlet('w', a=tt.stack([0.1,0.1,0.1,0.1,0.1]), shape=k) du = pm.DiscreteUniform('du',lower=0, upper=1,shape=k) idx_arr = [] for i in range(k): if du[i]: idx_arr.append(i) mu = pm.Norm…

Did this work for you @arraval - or did you find something else that worked? I’m facing the same issue…

@junpenglao Is there currently any support for dynamic shapes (i.e. using a tensorvariable as input to the shape argument)? I am trying to estimate the parameters of a Compound Poisson process with a Binomial distribution as the underlying distribution. The number of Binomial RVs follow a Poisson di…

We are definitely better at supporting dynamic shape, but I am not sure if your use case is supported yet. Could you open a new post with the example you have in mind?

Dirichlet with random shape

Questions

aclam February 14, 2023, 7:43am 8

@junpenglao Sounds good. I have opened a new post here, thanks!

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Dirichlet with random shape

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