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

.. py:method:: pastas.solver.mcmc.Emcee.log_likelihood(p: pastas.typing.ArrayLike, noise: bool, weights: pandas.Series | None = None, callback: pastas.typing.CallBack | None = None) -> float

   
   Log-likelihood function.

   :param p: Numpy array with the parameters.
   :type p: numpy.Array
   :param noise: If True, the noise model is applied to the residuals. This is passed on to
                 the misfit function, which will apply the noise model if True.
   :type noise: bool
   :param weights: Series with weights for the residuals. This is passed on to the misfit
                   function, which will apply the weights if provided.
   :type weights: pandas.Series, optional
   :param callback: Callback function that will be called after each iteration of the solver.
                    This is passed on to the misfit function, which will call the callback if
                    provided.
   :type callback: callable, optional

   :returns: **lnlike** -- The log-likelihood for the parameters.
   :rtype: float

   .. rubric:: Notes

   This method is always called by emcee.















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