gpflow.probability_distributions#

Classes#

gpflow.probability_distributions.DiagonalGaussian#

class gpflow.probability_distributions.DiagonalGaussian(mu, cov)[source]#

Bases: gpflow.probability_distributions.ProbabilityDistribution

Parameters

  • mu (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

  • cov (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

gpflow.probability_distributions.Gaussian#

class gpflow.probability_distributions.Gaussian(mu, cov)[source]#

Bases: gpflow.probability_distributions.ProbabilityDistribution

Parameters

  • mu (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

  • cov (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

gpflow.probability_distributions.MarkovGaussian#

class gpflow.probability_distributions.MarkovGaussian(mu, cov)[source]#

Bases: gpflow.probability_distributions.ProbabilityDistribution

Gaussian distribution with Markov structure. Only covariances and covariances between t and t+1 need to be parameterised. We use the solution proposed by Carl Rasmussen, i.e. to represent Var[x_t] = cov[x_t, :, :] * cov[x_t, :, :].T Cov[x_t, x_{t+1}] = cov[t, :, :] * cov[t+1, :, :]

Parameters

  • mu (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

  • cov (Union[ndarray[Any, Any], Tensor, Variable, Parameter]) –

gpflow.probability_distributions.ProbabilityDistribution#

class gpflow.probability_distributions.ProbabilityDistribution[source]#

Bases: object

This is the base class for a probability distributions, over which we take the expectations in the expectations framework.