pastas.solver.mcmc.Emcee
========================

.. toctree::
   :hidden:

   /api/pastas/solver/mcmc/Emcee.get_init_parameters
   /api/pastas/solver/mcmc/Emcee.solve
   /api/pastas/solver/mcmc/Emcee.log_probability
   /api/pastas/solver/mcmc/Emcee.log_likelihood
   /api/pastas/solver/mcmc/Emcee.log_prior
   /api/pastas/solver/mcmc/Emcee.fit_report
   /api/pastas/solver/mcmc/Emcee.to_dict

.. py:class:: pastas.solver.mcmc.Emcee(name: str = 'solver', objfunction: pastas.solver.likelihood.GaussianLikelihood | pastas.solver.likelihood.GaussianLikelihoodAr1 | None = GaussianLikelihood(), nwalkers: int = 20, backend: Any | None = None, moves: Any | None = None, parallel: bool = False, progress_bar: bool = True, **kwargs: Any)



   
   Solver based on MCMC approach in emcee :cite:p:`foreman-mackey_emcee_2013`.

   :param objfunction: An objective function to be minimized. See the pastas.likelihood module for
                       more information.
   :type objfunction: pastas.solver.likelihood function, optional
   :param nwalkers: Number of walkers to use. Default is 20.
   :type nwalkers: int, optional
   :param backend: One of the Backends from Emcee used to store MCMC results. See the Emcee
                   documentation for more information.
   :type backend: emcee.backend, optional
   :param moves: The moves argument determines how the next step for a walker is chosen in
                 the MCMC approach. One of the Moves classes from Emcee has to be provided.
                 See Emcee documentation for more information.
   :type moves: emcee.moves, optional
   :param parallel: Run the sampler in parallel or not.
   :type parallel: bool, optional
   :param progress_bar: Show the progress bar or not. Requires the `tqdm` package to be installed.
   :type progress_bar: bool, optional
   :param \*\*kwargs: All other keyword arguments are passed on to the SolverBase class.
   :param optional: All other keyword arguments are passed on to the SolverBase class.

   .. rubric:: Notes

   The EmceeSolve solver uses the emcee package to perform a Markov Chain Monte Carlo
   (MCMC) approach to find the optimal parameter values. The solver can be used as
   follows::

       solver = ps.solver.Emcee(nwalkers=20, progress_bar=True)
       ml.solve(solver=solver)

   The arguments provided are mostly passed on to the `emcee.EnsembleSampler`
   and determine how that instance is created. Arguments you want to pass on to
   `run_mcmc` (and indirectly the `sample` method), can be passed on to
   `Model.solve`, like::

       ml.solve(solver=ps.solver.Emcee(), thin_by=2)

   .. rubric:: Examples

   Example usage::

       ml.solve(solver=ps.solver.Emcee(), steps=5000)

   To obtain the MCMC chains, use::

       ml.solver.sampler.get_chain(flat=True, discard=3000)

   .. rubric:: References

   https://emcee.readthedocs.io/en/stable/

   .. seealso:: :py:obj:`emcee.EnsembleSampler`, :py:obj:`emcee.moves`, :py:obj:`emcee.backend`, :py:obj:`pastas.solver.objective_function`















   ..
       !! processed by numpydoc !!
Methods
-------

.. autoapisummary::

   pastas.solver.mcmc.Emcee.get_init_parameters
   pastas.solver.mcmc.Emcee.solve
   pastas.solver.mcmc.Emcee.log_probability
   pastas.solver.mcmc.Emcee.log_likelihood
   pastas.solver.mcmc.Emcee.log_prior
   pastas.solver.mcmc.Emcee.fit_report
   pastas.solver.mcmc.Emcee.to_dict