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Parameter Estimation

Maximum likelihood estimation, EM algorithm, residual diagnostics, and parameter transforms. See :doc:`/user-guide/estimation` for a guide on choosing between MLE and EM.

MLE

Gradient-based optimization of the log-likelihood using JAX autodiff. Flexible: supports any differentiable parameterization via a user-defined model_fn.

.. autofunction:: dynaris.estimation.mle.fit_mle


.. autoclass:: dynaris.estimation.mle.MLEResult
   :members:

EM Algorithm

Iterative variance estimation with guaranteed non-decreasing log-likelihood. Simpler setup than MLE --- just pass an initial model.

.. autofunction:: dynaris.estimation.em.fit_em


.. autoclass:: dynaris.estimation.em.EMResult
   :members:

Diagnostics

Tools for checking model adequacy after fitting.

.. autofunction:: dynaris.estimation.diagnostics.standardized_residuals


.. autofunction:: dynaris.estimation.diagnostics.acf


.. autofunction:: dynaris.estimation.diagnostics.pacf


.. autofunction:: dynaris.estimation.diagnostics.ljung_box

Transforms

Map unconstrained parameters to positive values for variance estimation.

.. autofunction:: dynaris.estimation.transforms.softplus


.. autofunction:: dynaris.estimation.transforms.inverse_softplus