def model_stats(self):
        """Returns the log likelihood ratio and log prior as a dict of arrays.

        The returned array has shape ntemps x nwalkers x niterations.

        Unfortunately, because ``emcee_pt`` does not have blob support, this
        will only return the loglikelihood and logprior (with the logjacobian
        set to zero) regardless of what stats the model can return.


        .. warning::
            Since the `logjacobian` is not saved by `emcee_pt`, the `logprior`
            returned here is the log of the prior pdf in the sampling
            coordinate frame rather than the variable params frame. This
            differs from the variable params frame by the log of the Jacobian
            of the transform from one frame to the other. If no sampling
            transforms were used, then the `logprior` is the same.
        """
        # likelihood has shape ntemps x nwalkers x niterations
        logl = self._sampler.lnlikelihood
        # get prior from posterior
        logp = self._sampler.lnprobability - logl
        logjacobian = numpy.zeros(logp.shape)
        return {'loglikelihood': logl, 'logprior': logp,
                'logjacobian': logjacobian}
Returns the log likelihood ratio and log prior as a dict of arrays.

        The returned array has shape ntemps x nwalkers x niterations.

        Unfortunately, because ``emcee_pt`` does not have blob support, this
        will only return the loglikelihood and logprior (with the logjacobian
        set to zero) regardless of what stats the model can return.


        .. warning::
            Since the `logjacobian` is not saved by `emcee_pt`, the `logprior`
            returned here is the log of the prior pdf in the sampling
            coordinate frame rather than the variable params frame. This
            differs from the variable params frame by the log of the Jacobian
            of the transform from one frame to the other. If no sampling
            transforms were used, then the `logprior` is the same.