Say we generate data by nesting a latent variable from a logistic model within a Poisson model (perhaps “nesting” is not the best word here, but I do not know a better one). For example: import pymc3 as pm import numpy as np # generate data rng = np.random.default_rng(12345678) # Parameters of in…

I think so. Here I increased the variance of x_1 by a factor of 4 and it seems to sample just fine: Google Colaboratory Now you might be unlucky in that your data actually looks like your original parameters, in which case you might need a simpler model (or much more regularization)

Sampling from nested models

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non87 May 27, 2021, 1:52pm 5

I have never seen such bad plots

I would conclude that yes, the sample does not converge in the right region. The pairwise plot shows that the parameters are really correlated. In particular d, c and b (well, 3 out of 4).

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Sampling from nested models

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