stats
===============

.. py:module:: jaxns.internals.stats

.. rubric:: :code:`jaxns.internals.stats`


.. rubric:: Module Contents

.. py:function:: normal_to_lognormal(mu, std)

   Convert normal parameters to log-normal parameters.

   :param mu: mean of normal RV
   :param std: standard deviation of normal RV

   :returns: mu, sigma of log-normal RV


.. py:function:: density_estimation(xstar, x, alpha=1.0 / 3.0, order=1)

   Estimates the density of xstar given x using a trick.

   :param xstar: array of points to estimate density at
   :param x: array of points to estimate density from
   :param alpha: power law exponent
   :param order: order of norm to use

   :returns: density at xstar


.. py:function:: linear_to_log_stats(log_f_mean, *, log_f2_mean=None, log_f_var=None)

   Converts normal to log-normal stats.
   :param log_f_mean: log(E(f))
   :param log_f2_mean: log(E(f**2))
   :param log_f_var: log(Var(f))

   :returns: E(log(f))
             Var(log(f))


.. py:function:: effective_sample_size_kish(log_Z_mean, log_dZ2_mean)

   Computes Kish's ESS = [sum dZ]^2 / [sum dZ^2]

   :param log_Z_mean:
   :param log_dZ2_mean:
   :return: