# bayesian_model.py
import pyro
import pyro.distributions as dist
import torch

def failure_model(observations=None):
    # Priors for component types
    switch_failure_rate = pyro.sample("switch_failure", dist.Beta(1, 10))
    server_failure_rate = pyro.sample("server_failure", dist.Beta(1, 20))
    # ... likelihood based on observations
    return switch_failure_rate, server_failure_rate