gpflow.probability_distributions¶

gpflow.probability_distributions.DiagonalGaussian¶

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

Bases: gpflow.probability_distributions.ProbabilityDistribution

Parameters

mu (Union[tensorflow.Tensor, tensorflow.Variable, Parameter]) –
cov (Union[tensorflow.Tensor, tensorflow.Variable, Parameter]) –

gpflow.probability_distributions.Gaussian¶

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

Bases: gpflow.probability_distributions.ProbabilityDistribution

Parameters

mu (Union[tensorflow.Tensor, tensorflow.Variable, Parameter]) –
cov (Union[tensorflow.Tensor, tensorflow.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[tensorflow.Tensor, tensorflow.Variable, Parameter]) –
cov (Union[tensorflow.Tensor, tensorflow.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.