mcmc_inversion.py
def evaluate_likelihood(self, mtrial):
        """
        Evaluates the likelihood of our observations, given a
        proposed model, mtrial
        """
        # Find the predicted observations for mtrial
        d_predicted = self.predict_observations(mtrial)
        # Calculate the vector of residuals between the predicted and
        # actual observations
        residuals = self.observations - d_predicted
        # Make residuals a "sparse" matrix so we can multiply with cd_inv
        exponent = np.dot((residuals * self.weights),residuals)
        # Calculate the likelihood
        likelihood = -0.5 * exponent

        return likelihood

        Evaluates the likelihood of our observations, given a
        proposed model, mtrial
        
Evaluates the likelihood of our observations, given a
proposed model, mtrial
Evaluates the likelihood of our observations given a proposed model mtrial
def evaluate_likelihood(self, mtrial):
        d_predicted = self.predict_observations(mtrial)
        residuals = self.observations - d_predicted
        exponent = np.dot((residuals * self.weights),residuals)
        likelihood = -0.5 * exponent
        return likelihood
def evaluate_likelihood ( self , mtrial ) : d_predicted = self . predict_observations ( mtrial ) residuals = self . observations - d_predicted exponent = np . dot ( ( residuals * self . weights ) , residuals ) likelihood = - 0.5 * exponent return likelihood
Evaluates the likelihood of our observations, given a
proposed model, mtrial
Evaluates the likelihood of our observations given a proposed model mtrial
"""
        Evaluates the likelihood of our observations, given a
        proposed model, mtrial
        """ # Find the predicted observations for mtrial # Calculate the vector of residuals between the predicted and # actual observations # Make residuals a "sparse" matrix so we can multiply with cd_inv # Calculate the likelihood
self
mtrial