`altar.models.linear.Linear`

Module Contents

Classes

class altar.models.linear.Linear.Linear(name, locator, **kwds)

Bases: altar.models.bayesian

parameters

doc = the number of parameters in the model

observations

doc = the number of data samples

prep

default

doc = the distribution used to generate the initial sample

prior

default

doc = the prior distribution

norm

default

doc = the norm to use when computing the data log likelihood

case

doc = the directory with the input files

green

doc = the name of the file with the Green functions

data

doc = the name of the file with the observations

cd

doc = the name of the file with the data covariance matrix

ifs

G

d

Cd

Cd_inv

residuals

normalization = 1

initialize(self, application): Initialize the state of the model given a {problem} specification

initializeSample(self, step): Fill {step.θ} with an initial random sample from my prior distribution.

priorLikelihood(self, step): Fill {step.prior} with the likelihoods of the samples in {step.theta} in the prior distribution

dataLikelihood(self, step): Fill {step.data} with the likelihoods of the samples in {step.theta} given the available data. This is what is usually referred to as the “forward model”

verify(self, step, mask): Check whether the samples in {step.theta} are consistent with the model requirements and update the {mask}, a vector with zeroes for valid samples and non-zero for invalid ones

mountInputDataspace(self, pfs): Mount the directory with my input files

loadInputs(self): Load the data in the input files into memory

computeCovarianceInverse(self, cd): Compute the inverse of the data covariance matrix

computeNormalization(self, observations, cd): Compute the normalization of the L2 norm

initializeResiduals(self, samples, data): Prime the matrix that will hold the residuals (G θ - d) for each sample by duplicating the observation vector as many times as there are samples