.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/latent/likelihood_discrete.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_latent_likelihood_discrete.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_latent_likelihood_discrete.py:


Likelihood function
===================

Calculates the contributions to the log likelihood of each indicator

Michel Bierlaire
Wed Sept 03 2025, 08:15:53

.. GENERATED FROM PYTHON SOURCE LINES 11-44

.. code-block:: Python


    from measurement_equations_continuous import MeasurementEquation

    from biogeme.exceptions import BiogemeError
    from biogeme.expressions import Elem, Expression, MultipleProduct, Numeric, Variable
    from biogeme.models import ordered_probit_from_thresholds


    def likelihood_discrete_mimic(
        measurement_equations: dict[str, MeasurementEquation],
        threshold_parameters: list[Expression],
        discrete_values: list[int],
        missing_values: list[int],
    ) -> Expression:
        list_of_factors = []
        for indicator, measurement_equation in measurement_equations.items():
            dict_of_probabilities = ordered_probit_from_thresholds(
                continuous_value=measurement_equation.intercept
                + measurement_equation.linear_terms,
                scale_parameter=measurement_equation.scale_parameter,
                list_of_discrete_values=discrete_values,
                threshold_parameters=threshold_parameters,
            )
            for index in missing_values:
                if index in discrete_values:
                    err_msg = (
                        f'Index {index} cannot be both a Likert index and a missing value'
                    )
                    raise BiogemeError(err_msg)
                dict_of_probabilities[index] = Numeric(1.0)
            list_of_factors.append(Elem(dict_of_probabilities, Variable(indicator)))

        return MultipleProduct(list_of_factors)


.. _sphx_glr_download_auto_examples_latent_likelihood_discrete.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: likelihood_discrete.ipynb <likelihood_discrete.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: likelihood_discrete.py <likelihood_discrete.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: likelihood_discrete.zip <likelihood_discrete.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_