neuroprob/base.py
def compute_F(self, XZ):
        """
        Computes the posterior over :mathm:`F`, conditioned on the data. In most cases, this is amortized via 
        learned weights/parameters of some approximate posterior. In ML/MAP settings the approximation is a 
        delta distribution, meaning there is no variational uncertainty in the mapping.
        """
        raise NotImplementedError

        Computes the posterior over :mathm:`F`, conditioned on the data. In most cases, this is amortized via 
        learned weights/parameters of some approximate posterior. In ML/MAP settings the approximation is a 
        delta distribution, meaning there is no variational uncertainty in the mapping.
        
Computes the posterior over :mathm:`F`, conditioned on the data. In most cases, this is amortized via
learned weights/parameters of some approximate posterior. In ML/MAP settings the approximation is a
delta distribution, meaning there is no variational uncertainty in the mapping.
Computes the posterior over : mathm : ` F ` conditioned on the data . In most cases this is amortized via learned weights / parameters of some approximate posterior . In ML / MAP settings the approximation is a delta distribution meaning there is no variational uncertainty in the mapping .
def compute_F(self, XZ):
        raise NotImplementedError
def compute_F ( self , XZ ) : raise NotImplementedError
Computes the posterior over :mathm:`F`, conditioned on the data.
Computes the posterior over : mathm : ` F ` conditioned on the data .
"""
        Computes the posterior over :mathm:`F`, conditioned on the data. In most cases, this is amortized via 
        learned weights/parameters of some approximate posterior. In ML/MAP settings the approximation is a 
        delta distribution, meaning there is no variational uncertainty in the mapping.
        """
self
XZ