Create bayesian_model.py

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