neuroprob/utils/stats.py
def modulated_count_dist(count_dist, mult_gain, add_gain, samples):
    """
    Generate distribution samples from the modulated count process with additive and multiplicate gains.
    
    .. math::
            P_{mod}(n|\lambda) = \int P_{count}(n|G_{mul}\lambda + G_{add}) P(G_{mu]}, G_{add}) \mathrm{d}G_{mul} \mathrm{d}G_{add},

        where :math:`p(y \mid f)` is the likelihood.
    
    References:
    
    [1] `Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability`,
         Adam S. Charles, Mijung Park, J. PatrickWeller, Gregory D. Horwitz, Jonathan W. Pillow (2018)
         
    """
    
    raise NotImplementedError

    Generate distribution samples from the modulated count process with additive and multiplicate gains.
    
    .. math::
            P_{mod}(n|\lambda) = \int P_{count}(n|G_{mul}\lambda + G_{add}) P(G_{mu]}, G_{add}) \mathrm{d}G_{mul} \mathrm{d}G_{add},

        where :math:`p(y \mid f)` is the likelihood.
    
    References:
    
    [1] `Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability`,
         Adam S. Charles, Mijung Park, J. PatrickWeller, Gregory D. Horwitz, Jonathan W. Pillow (2018)
         
    
Generate distribution samples from the modulated count process with additive and multiplicate gains.





[1] `Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability`,
Adam S. Charles, Mijung Park, J.
Generate distribution samples from the modulated count process with additive and multiplicate gains . [ 1 ] ` Dethroning the Fano Factor : A Flexible Model - Based Approach to Partitioning Neural Variability ` Adam S . Charles Mijung Park J .
def modulated_count_dist(count_dist, mult_gain, add_gain, samples):
    raise NotImplementedError
def modulated_count_dist ( count_dist , mult_gain , add_gain , samples ) : raise NotImplementedError
Generate distribution samples from the modulated count process with additive and multiplicate gains.
Generate distribution samples from the modulated count process with additive and multiplicate gains .
"""
    Generate distribution samples from the modulated count process with additive and multiplicate gains.
    
    .. math::
            P_{mod}(n|\lambda) = \int P_{count}(n|G_{mul}\lambda + G_{add}) P(G_{mu]}, G_{add}) \mathrm{d}G_{mul} \mathrm{d}G_{add},

        where :math:`p(y \mid f)` is the likelihood.
    
    References:
    
    [1] `Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability`,
         Adam S. Charles, Mijung Park, J. PatrickWeller, Gregory D. Horwitz, Jonathan W. Pillow (2018)
         
    """
count_dist
mult_gain
add_gain
samples