Commit 42523fb1 authored by Moritz Huebner's avatar Moritz Huebner

Bayesian model dimensionality

parent b26a5a98
......@@ -535,6 +535,19 @@ class Result(object):
return self.posterior_volume / self.prior_volume(priors)
def bayesian_model_dimensionality(self):
""" Characterises how many parameters are effectively constraint by the data
See <>
float: The model dimensionality
return 2 * (np.mean(self.posterior['log_likelihood']**2) -
def get_one_dimensional_median_and_error_bar(self, key, fmt='.2f',
quantiles=(0.16, 0.84)):
""" Calculate the median and error bar for a given key
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