Hi there - I am experiencing the same slowness to the extreme and was wondering if someone had more insights since the last above post in Oct 2022. Truncated Normal (straight from pymc) likelihood was manageable (15min per sampling, I have 99 to do) but Truncated Student likelihood (own build from pymc, which could be the problem) gets stuck (see pic below on the sampling status).
Here is the model, with the 2 likelihood variants:
ndims = len(feature_cols)
X_shape = np.empty((0, ndims))
y_shape = np.empty((0,))
with pymc.Model() as MODEL:
# data containers
X = pymc.MutableData(“X”, X_shape)
y = pymc.MutableData(“y”, y_shape)
# priors
intercept = pymc.HalfNormal("intercept", sigma=1)
b = pymc.MvNormal("b", mu=np.zeros(ndims), cov=5 * np.eye(ndims))
nu = pymc.Gamma("nu", alpha=2.0, beta=0.1)
sigma = pymc.HalfCauchy("sigma", beta=10)
mu = intercept + pymc.math.dot(X, b).flatten()
# likelihood Truncated Normal:
likelihood = pymc.TruncatedNormal("obs", mu=mu, sigma=sigma, lower=0.0, observed=y)
# likelihood Truncated TStudent:
likelihood = pymc.Truncated('obs', pymc.StudentT.dist(mu=mu, nu=nu, sigma=sigma), observed=y, lower=0.0)
Thank you!