import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
from pymc.math import exp

%load_ext lab_black

5. Time-to-event Models: Example 2*

censored = np.array([np.inf, np.inf, 1, np.inf, 3])
y = np.array([2, 3, 1, 2.5, 3])

with pm.Model() as m:
    λ = pm.Gamma("λ", 2, 3)
    α = 1.5
    β = λ ** (-1 / α)
    obs_latent = pm.Weibull.dist(α, β)
    likelihood = pm.Censored(
        "likelihood", obs_latent, lower=None, upper=censored, observed=y
    )

    trace = pm.sample(1000)

az.summary(trace)

Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [λ]

100.00% [8000/8000 00:00<00:00 Sampling 4 chains, 0 divergences]

Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.

	mean	sd	hdi_3%	hdi_97%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
λ	0.234	0.104	0.064	0.423	0.002	0.002	1624.0	1838.0	1.0

%load_ext watermark
%watermark -n -u -v -iv -p pytensor

Last updated: Wed Mar 22 2023

Python implementation: CPython
Python version       : 3.11.0
IPython version      : 8.9.0

pytensor: 2.10.1

pandas    : 1.5.3
matplotlib: 3.6.3
pymc      : 5.1.2
numpy     : 1.24.2
arviz     : 0.14.0