Callables and functions in Python: a simple explanation with an example
5 min readMay 14, 2022
When I read the book Bayesian Methods for Hackers I found many times code like this.
N = rfm_cal_holdout.shape[0]
x = rfm_cal_holdout['frequency_cal'].values
t_x = rfm_cal_holdout['recency_cal'].values
T = rfm_cal_holdout['T_cal'].values bgnbd_model = pm.Model()
with bgnbd_model: def logp(x, t_x, T):
"""
Loglikelihood function
"""
# Bla bla
# Some fancy stuff that calculates the value of A3
return A3 # This is the important part --------------------|
# v
loglikelihood = pm.DensityDist("loglikelihood", logp, observed= {'x': x, 't_x': t_x, 'T': T})
I knew that functions were treated as first-class objects by Python, nevertheless, I was a bit confused when I read this piece of code. The questions that came to my mind were like.
- Why is `pm.DensityDist` receiving logp as a parameter?
- Where are logp parameters?
- How in the world variables x, t_x, and T are passed so that logp resolves to a value? Is it because they are declared above and somehow “are passed” into logp function instance?
- Why is this thing working in…
