gpflow.logdensities#

Functions#

gpflow.logdensities.bernoulli#

gpflow.logdensities.bernoulli(x, p)[source]#
Parameters:

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

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

Return type:

Tensor

gpflow.logdensities.beta#

gpflow.logdensities.beta(x, alpha, beta)[source]#
Parameters:

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

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

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

Return type:

Tensor

gpflow.logdensities.exponential#

gpflow.logdensities.exponential(x, scale)[source]#
Parameters:

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

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

Return type:

Tensor

gpflow.logdensities.gamma#

gpflow.logdensities.gamma(x, shape, scale)[source]#
Parameters:

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

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

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

Return type:

Tensor

gpflow.logdensities.gaussian#

gpflow.logdensities.gaussian(x, mu, var)[source]#
Parameters:

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

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

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

Return type:

Tensor

gpflow.logdensities.laplace#

gpflow.logdensities.laplace(x, mu, sigma)[source]#
Parameters:

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

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

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

Return type:

Tensor

gpflow.logdensities.lognormal#

gpflow.logdensities.lognormal(x, mu, var)[source]#
Parameters:

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

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

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

Return type:

Tensor

gpflow.logdensities.multivariate_normal#

gpflow.logdensities.multivariate_normal(x, mu, L)[source]#

Computes the log-density of a multivariate normal.

Parameters:

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

    • x has shape [D, broadcast N].

    sample(s) for which we want the density

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

    • mu has shape [D, broadcast N].

    mean(s) of the normal distribution

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

    • L has shape [D, D].

    Cholesky decomposition of the covariance matrix

Return type:

Tensor

Returns:

  • return has shape [N].

log densities

gpflow.logdensities.poisson#

gpflow.logdensities.poisson(x, lam)[source]#
Parameters:

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

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

Return type:

Tensor

gpflow.logdensities.student_t#

gpflow.logdensities.student_t(x, mean, scale, df)[source]#
Parameters:

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

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

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

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

Return type:

Tensor