pytorch_hamiliton_unittest

To test Hamilton filter probability invariants in PyTorch, use Pytest to assert that filtered state probabilities are non-negative and sum to 1.0 at every time step during forward-pass inference. This ensures your regime-switching model maintains valid distributions across batched time-series inputs.

Validating tensor shapes and parameter reparameterization for PyTorch Markov regime-switching training involves asserting expected tensor dimensions for batched inputs and enforcing domain constraints, such as strictly positive sigma values and normalized, non-negative transition matrix rows.

Checking regime identifiability sorting consistency requires verifying that identical permutations apply across means, sigma parameters, and 3D transition tensors. This permutation equivariance ensures sorted regime states remain mathematically equivalent during vectorized PyTorch operations.

Yes, you can use Pytest to catch tensor-shape regressions in vectorized PyTorch Hamilton filter inference by asserting expected tensor dimensions for batched time-series inputs and raising errors for mismatched inputs during next-step prediction.

Your Markov regime-switching model fails probability distribution constraints during unit testing when transition rows do not sum to 1.0, sigma values drop to zero or below, or filtered probabilities violate non-negativity and normalization rules across time steps.

When unit testing a PyTorch Markov regime-switching model, enforce that sigma is strictly positive, transition rows are non-negative and sum to 1.0, filtered probabilities sum to 1.0 per time step, and identifiability sorting applies identical permutations across all parameter tensors.