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docs: Added resampled M-test and MLL test to the concepts section. Left Serafini et al., 2024 as in-prep, until pre-print is submitted.
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docs/concepts/evaluations.rst

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@@ -30,7 +30,7 @@ consistency tests and they verify whether a forecast in consistent with an obser
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that can be used to compare the performance of two (or more) competing forecasts.
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PyCSEP implements the following evaluation routines for grid-based forecasts. These functions are intended to work with
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:class:`GriddedForecasts<csep.core.forecasts.GriddedForecast>` and :class:`CSEPCatalogs`<csep.core.catalogs.CSEPCatalog>`.
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Visit the :ref:`catalogs reference<catalogs-reference>` and the :ref:`forecasts reference<forecasts-reference>` to learn
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Visit the :ref:`catalogs reference<catalogs-reference>` and the :ref:`forecasts reference<forecast-reference>` to learn
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more about to import your forecasts and catalogs into PyCSEP.
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.. note::
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magnitude_test
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pseudolikelihood_test
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calibration_test
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resampled_magnitude_test
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MLL_magnitude_test
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Publication reference
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=====================
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3. Magnitude test (:ref:`Savran et al., 2020<savran-2020>`)
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4. Pseudolikelihood test (:ref:`Savran et al., 2020<savran-2020>`)
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5. Calibration test (:ref:`Savran et al., 2020<savran-2020>`)
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6. Resampled Magnitude Test (Serafini et al., in-prep)
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7. MLL Magnitude Test (Serafini et al., in-prep)
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****************************
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Preparing evaluation catalog
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****************************
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The evaluations in PyCSEP do not implicitly filter the observed catalogs or modify the forecast data when called. For most
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cases, the observation catalog should be filtered according to:
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1. Magnitude range of the forecast
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2. Spatial region of the forecast
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3. Start and end-time of the forecast

docs/getting_started/theory.rst

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Resampled Magnitude Test
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Aim: Perform the resampled magnitude test for catalog-based forecasts (Serafini et al., 2024),
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Aim: Perform the resampled magnitude test for catalog-based forecasts (Serafini et al., , in-prep),
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which is a correction to the original M-test implementation that is biased to the total N of
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events.
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Modified Multinomial Log-Likelihood (MLL) Magnitude Test
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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* Aim: Implements the modified Multinomial log-likelihood (MLL) magnitude test (Serafini et al., 2024).
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* Aim: Implements the modified Multinomial log-likelihood (MLL) magnitude test (Serafini et al., in-prep).
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* Method: Calculates the test statistic distribution as:
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