information_gain#

cosmicjellyfish.analysis.fisher_operations.information_gain(fisher_1, fisher_2, fisher_prior, units=0.6931471805599453, stat=True)[source]#

This function computes the Fisher approximation of Kullback-Leibler information gain. For the details of the formula we refer to the CosmicFish notes.

Parameters:

  • fisher_1 (cosmicfish_pylib.fisher_matrix.fisher_matrix) – first input Fisher matrix

  • fisher_2 (cosmicfish_pylib.fisher_matrix.fisher_matrix) – second input Fisher matrix

  • fisher_prior (cosmicfish_pylib.fisher_matrix.fisher_matrix) – input Fisher matrix with the prior information.

  • units (float) – Units of information gain. Optional by default in Bits.

  • stat (logical) – wether to output the expected value and variance

Returns:

a float with the information gain.

Return type:

float