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Create bayesian_model.py
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# 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