Closed ikibkilam closed 5 years ago
You've posted this issue to the wrong project. Please post to the PyMC3 issue tracker
Reading this issue further, I would recommend posting to the Discourse site rather than as an issue, as this is a usage question rather than a bug report.
I am new to PYMC3. While learning, I did a Mixture Model tutorial, outlined in the book "Probabilistic Programming & Bayesian Methods for Hackers". I believe I am doing exactly what was outlined in the tutorial, however my sampling does not start or is very slow. I tried various permutations, as below, without success. Note, the data size is 300. Could someone tell me what I am doing wrong?
with pm.Model() as model:
Which normal distribution?
First, I let PyMC decide which routines to use and saw no results in 5 hours:
with model: trace = pm.sample(25000) Multiprocess sampling (4 chains in 4 jobs) CompoundStep
I also tried to be more prescriptive and attempted the following, but the sampler is very slow: with model: start = find_MAP(model = model) step1 = pm.Metropolis(vars = [p, sdevs, means]) step2 = pm.ElemwiseCategorical(vars = [assign]) trace = pm.sample(25000, start = start, step = [step1, step2]) logp = -4.7305e+05, ||grad|| = 299.51: 100%|███████████████████████████████████████████| 39/39 [01:37<00:00, 2.51s/it] C:\Users\bikim\AppData\Local\conda\conda\envs\pymc3p36\lib\site-packages\ipykernel_launcher.py:6: DeprecationWarning: ElemwiseCategorical is deprecated, switch to CategoricalGibbsMetropolis.
Multiprocess sampling (4 chains in 4 jobs) CompoundStep
This should be an easy problem. I appreciate any insight into what I am doing wrong.