botorch.posteriors

botorch.posteriors.posterior

Abstract base module for all botorch posteriors.

Posterior

class botorch.posteriors.posterior.Posterior[source]

Abstract base class for botorch posteriors.

device

The torch device of the posterior.

Return type:device
dtype

The torch dtype of the posterior.

Return type:dtype
event_shape

The event shape (i.e. the shape of a single sample).

Return type:Size
mean

The mean of the posterior as a (b) x n x o-dim Tensor.

Return type:Tensor
rsample(sample_shape=None, base_samples=None)[source]

Sample from the posterior (with gradients).

Parameters:
  • sample_shape (Optional[Size]) – A torch.Size object specifying the sample shape. To draw n samples, set to torch.Size([n]). To draw b batches of n samples each, set to torch.Size([b, n]).
  • base_samples (Optional[Tensor]) – An (optional) Tensor of N(0, I) base samples of appropriate dimension, typically obtained from a Sampler. This is used for deterministic optimization.
Return type:

Tensor
Returns:

A sample_shape x event-dim Tensor of samples from the posterior.
sample(sample_shape=None, base_samples=None)[source]

Sample from the posterior (without gradients).

This is a simple wrapper calling rsample using with torch.no_grad().

Parameters:
  • sample_shape (Optional[Size]) – A torch.Size object specifying the sample shape. To draw n samples, set to torch.Size([n]). To draw b batches of n samples each, set to torch.Size([b, n]).
  • base_samples (Optional[Tensor]) – An (optional) Tensor of N(0, I) base samples of appropriate dimension, typically obtained from a Sampler object. This is used for deterministic optimization.
Return type:

Tensor
Returns:

A sample_shape x event_shape-dim Tensor of samples from the posterior.
variance

The variance of the posterior as a (b) x n x o-dim Tensor.

Return type:Tensor

botorch.posteriors.gpytorch

Posterior Module to be used with GPyTorch models.

GPyTorchPosterior

class botorch.posteriors.gpytorch.GPyTorchPosterior(mvn)[source]

A posterior based on GPyTorch’s multi-variate Normal distributions.

A posterior based on GPyTorch’s multi-variate Normal distributions.

Parameters:mvn (MultivariateNormal) – A GPyTorch MultivariateNormal (single-output case) or MultitaskMultivariateNormal (multi-output case).
device

The torch device of the posterior.

Return type:device
dtype

The torch dtype of the posterior.

Return type:dtype
event_shape

The event shape (i.e. the shape of a single sample) of the posterior.

Return type:Size
mean

The posterior mean.

Return type:Tensor
rsample(sample_shape=None, base_samples=None)[source]

Sample from the posterior (with gradients).

Parameters:
  • sample_shape (Optional[Size]) – A torch.Size object specifying the sample shape. To draw n samples, set to torch.Size([n]). To draw b batches of n samples each, set to torch.Size([b, n]).
  • base_samples (Optional[Tensor]) – An (optional) Tensor of N(0, I) base samples of appropriate dimension, typically obtained from a Sampler. This is used for deterministic optimization.
Return type:

Tensor
Returns:

A sample_shape x event_shape-dim Tensor of samples from the posterior.
variance

The posterior variance.

Return type:Tensor