mcmc_inversion.py
def gaussian_mtrial(self, mcurr, std):
        """
        Generates a proposal model from a small perturbation
        of the current model, mcurr. The perturbation is drawn from
        a Gaussian distribution with zero mean and standard
        deviation = std
        """
        # Generate the model perturbation
        perturb = np.random.normal(0.0, std, len(mcurr))
        # Perturb the current model
        mtrial = mcurr + perturb

        return mtrial

        Generates a proposal model from a small perturbation
        of the current model, mcurr. The perturbation is drawn from
        a Gaussian distribution with zero mean and standard
        deviation = std
        
Generates a proposal model from a small perturbation
of the current model, mcurr. The perturbation is drawn from
a Gaussian distribution with zero mean and standard
deviation = std
Generates a proposal model from a small perturbation of the current model mcurr . The perturbation is drawn from a Gaussian distribution with zero mean and standard deviation = std
def gaussian_mtrial(self, mcurr, std):
        perturb = np.random.normal(0.0, std, len(mcurr))
        mtrial = mcurr + perturb
        return mtrial
def gaussian_mtrial ( self , mcurr , std ) : perturb = np . random . normal ( 0.0 , std , len ( mcurr ) ) mtrial = mcurr + perturb return mtrial
Generates a proposal model from a small perturbation
of the current model, mcurr.
Generates a proposal model from a small perturbation of the current model mcurr .
"""
        Generates a proposal model from a small perturbation
        of the current model, mcurr. The perturbation is drawn from
        a Gaussian distribution with zero mean and standard
        deviation = std
        """ # Generate the model perturbation # Perturb the current model
self
mcurr
std