Create bayesian_model.py

bayesian_model.py

@@ -1,11 +1,27 @@
-# 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):
-    # Priors for component types
+    """
+    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))
-    # ... likelihood based on observations
-    return switch_failure_rate, server_failure_rate
+    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
+    }