gpflow.probability_distributions¶
gpflow.probability_distributions.DiagonalGaussian¶
-
class
gpflow.probability_distributions.
DiagonalGaussian
(mu, cov)[source]¶ Bases:
gpflow.probability_distributions.ProbabilityDistribution
- Parameters
mu (
Union
[Tensor
,Variable
,Parameter
]) –cov (
Union
[Tensor
,Variable
,Parameter
]) –
gpflow.probability_distributions.Gaussian¶
-
class
gpflow.probability_distributions.
Gaussian
(mu, cov)[source]¶ Bases:
gpflow.probability_distributions.ProbabilityDistribution
- Parameters
mu (
Union
[Tensor
,Variable
,Parameter
]) –cov (
Union
[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
[Tensor
,Variable
,Parameter
]) –cov (
Union
[Tensor
,Variable
,Parameter
]) –