Using Laplace as likelihood distribution

Questions
4

I can’t reproduce any problem from your code snippet. This runs completely fine for me (pymc3==3.11.4).

import pymc3 as pm
import numpy as np

data = np.random.normal(size=3)

with pm.Model() as model_0:
    # Prior Distributions for unknown model parameters:
    b_0 = pm.HalfNormal('b_0', sd=5)
    mu_0 = pm.Normal('mu_0', mu=0, sd=5)

    # Observed data is from a Likelihood distributions (Likelihood (sampling distribution) of observations):
    observed_data_0 = pm.Laplace('observed_data_0', mu=mu_0, b=b_0, observed=data)

    # Printing the result of log_likelihood:
    # print('log_likelihood result:', model_0)

    # draw 5000 posterior samples
    trace_0 = pm.sample(draws=1000, tune=1000, chains=3, cores=1, progressbar=True)

    # Obtaining Posterior Predictive Sampling:
    post_pred_0 = pm.sample_posterior_predictive(trace_0, samples=1000)
    print(post_pred_0['observed_data_0'].shape)
    print('\nSummary: ')
    print(pm.stats.summary(data=trace_0))
    print(pm.stats.summary(data=post_pred_0))