bayesian_model.py

"""
Pyro model for online learning of component failure probabilities.
"""
import pyro
import pyro.distributions as dist
import torch

def failure_model(observations=None):
    """
    Bayesian model for component failure rates.
    observations: tensor of observed failures (0/1) for each component.
    """
    # Priors for different component types
    switch_failure_rate = pyro.sample("switch_failure", dist.Beta(1, 10))
    server_failure_rate = pyro.sample("server_failure", dist.Beta(1, 20))
    service_failure_rate = pyro.sample("service_failure", dist.Beta(1, 5))
    
    # If observations provided, condition on them
    if observations is not None:
        with pyro.plate("components", len(observations)):
            pyro.sample("obs", dist.Bernoulli(probs=...), obs=observations)
    
    return {
        "switch": switch_failure_rate,
        "server": server_failure_rate,
        "service": service_failure_rate
    }