Adding the New Metropolis_Hasting Algorithm

Author: imSanko

dynamic_programming/Metropolis_Hasting.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+def metropolis_hastings(target_distribution, proposal_distribution, num_samples, initial_state):
+    samples = [initial_state]
+    current_state = initial_state
+
+    for _ in range(num_samples):
+        proposed_state = proposal_distribution(current_state)
+        acceptance_ratio = min(1, target_distribution(proposed_state) / target_distribution(current_state))
+
+        if np.random.rand() < acceptance_ratio:
+            current_state = proposed_state
+
+        samples.append(current_state)
+
+    return samples[1:]
+
+def target_distribution(x):
+    return np.exp(-x**2 / 2) / np.sqrt(2 * np.pi)
+
+def proposal_distribution(x):
+    return x + np.random.normal(0, 1)
+
+if __name__ == "__main__":
+    num_samples = 10000
+    initial_state = 0
+
+    samples = metropolis_hastings(target_distribution, proposal_distribution, num_samples, initial_state)