altar.models.gaussian.Gaussian
Module Contents
Classes
- class altar.models.gaussian.Gaussian.Gaussian(**kwds)
Bases:
altar.models.bayesianA model that emulates the probability density for a single observation of the model parameters. The observation is treated as normally distributed around a given mean, with a covariance constructed out of its eigenvalues and a rotation in configuration space. Currently, only two dimensional parameter spaces are supported.
- parameters
- doc = the number of model degrees of freedom
- support
- doc = the support interval of the prior distribution
- prep
- doc = the distribution used to generate the initial sample
- prior
- doc = the prior distribution
- μ
- doc = the location of the central value of the observation
- λ
- doc = the eigenvalues of the covariance matrix
- φ
- doc = the orientation of the covariance semi-major axis
- peak
- σ_inv
- 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