Let MEIS return a Proposal

isssm/estimation.py

@@ -194,17 +194,17 @@ def mle_pgssm(
     def f(theta, key):
         model = model_fn(theta, aux)
 
-        proposal, info = laplace_approximation(y, model, n_iter_la)
+        propsal_la, _ = laplace_approximation(y, model, n_iter_la)
 
         key, subkey = jrn.split(key)
-        z, Omega = modified_efficient_importance_sampling(
-            y, model, proposal.z, proposal.Omega, n_iter_la, N, subkey
+        proposal_meis, _ = modified_efficient_importance_sampling(
+            y, model, propsal_la.z, propsal_la.Omega, n_iter_la, N, subkey
         )
 
         key, subkey = jrn.split(key)
         # improve numerical stability by dividing by number of observations
         n_obs = y.size
-        return pgnll(y, model, z, Omega, N, subkey) / n_obs
+        return pgnll(y, model, proposal_meis.z, proposal_meis.Omega, N, subkey) / n_obs
 
     key, subkey = jrn.split(key)
     result = minimize_scipy(f, theta0, method="BFGS", options=options, args=(subkey,))

isssm/modified_efficient_importance_sampling.py

@@ -17,7 +17,7 @@
 from .util import MVN_degenerate as MVN, mm_sim
 
 from .glssm import mm_sim
-from .typing import GLSSM, PGSSM
+from .typing import GLSSM, PGSSM, GLSSMProposal, ConvergenceInformation
 
 
 @jit
@@ -67,7 +67,7 @@ def modified_efficient_importance_sampling(
     N: int,  # number of samples
     key: PRNGKeyArray,  # random key
     eps: Float = 1e-5,  # convergence threshold
-):
+) -> tuple[GLSSMProposal, ConvergenceInformation]:
     z, Omega = z_init, Omega_init
 
     np1, p, m = model.B.shape
@@ -124,10 +124,32 @@ def _iteration(val):
 
     _keep_going = lambda *args: jnp.logical_not(_break(*args))
 
-    n_iters, z, Omega, _, _ = while_loop(
+    n_iters, z, Omega, z_old, Omega_old = while_loop(
         _keep_going,
         _iteration,
         (0, z_init, Omega_init, jnp.empty_like(z_init), jnp.empty_like(Omega_init)),
     )
 
-    return z, Omega
+    proposal = GLSSMProposal(
+        u=model.u,
+        A=model.A,
+        D=model.D,
+        Sigma0=model.Sigma0,
+        Sigma=model.Sigma,
+        v=model.v,
+        B=model.B,
+        Omega=Omega,
+        z=z,
+    )
+
+    delta_z = jnp.max(jnp.abs(z - z_old))
+    delta_Omega = jnp.max(jnp.abs(Omega - Omega_old))
+    information = ConvergenceInformation(
+        converged=jnp.logical_and(
+            converged(z, z_old, eps), converged(Omega, Omega_old, eps)
+        ),
+        n_iter=n_iters,
+        delta=jnp.max(jnp.array([delta_z, delta_Omega])),
+    )
+
+    return proposal, information