Tutorials:
Examples:
DataLoader
DSPPHiddenLayer
DSPP
Package Reference
ExactGP
ExactQEP
ApproximateGP
ApproximateQEP
DeepGP
DeepGPLayer
DeepQEP
DeepQEPLayer
BayesianGPLVM
PointLatentVariable
MAPLatentVariable
VariationalLatentVariable
BayesianQEPLVM
PyroGP
PyroQEP
PDESolver
Likelihood
Likelihood.__call__()
Likelihood.expected_log_prob()
Likelihood.forward()
Likelihood.log_marginal()
Likelihood.marginal()
Likelihood.pyro_guide()
Likelihood.pyro_model()
GaussianLikelihood
GaussianLikelihoodWithMissingObs
FixedNoiseGaussianLikelihood
DirichletClassificationLikelihood
QExponentialLikelihood
QExponentialLikelihoodWithMissingObs
FixedNoiseQExponentialLikelihood
QExponentialDirichletClassificationLikelihood
BernoulliLikelihood
BetaLikelihood
LaplaceLikelihood
StudentTLikelihood
MultitaskGaussianLikelihood
HadamardGaussianLikelihood
MultitaskQExponentialLikelihood
HadamardQExponentialLikelihood
SoftmaxLikelihood
Kernel
Kernel.__call__()
Kernel.__getitem__()
Kernel.covar_dist()
Kernel.expand_batch()
Kernel.forward()
Kernel.named_sub_kernels()
Kernel.num_outputs_per_input()
Kernel.sub_kernels()
ConstantKernel
CosineKernel
CylindricalKernel
LinearKernel
MaternKernel
PeriodicKernel
PiecewisePolynomialKernel
PolynomialKernel
PolynomialKernelGrad
RBFKernel
RQKernel
SpectralDeltaKernel
SpectralMixtureKernel
AdditiveKernel
MultiDeviceKernel
AdditiveStructureKernel
ProductKernel
ProductStructureKernel
ScaleKernel
ArcKernel
IndexKernel
LCMKernel
MultitaskKernel
RBFKernelGrad
RBFKernelGradGrad
RQKernelGrad
RQKernelGradGrad
Matern32KernelGrad
Matern52KernelGrad
Matern52KernelGradGrad
GridKernel
GridInterpolationKernel
InducingPointKernel
RFFKernel
Mean
ZeroMean
ConstantMean
LinearMean
MultitaskMean
ConstantMeanGrad
ConstantMeanGradGrad
LinearMeanGrad
LinearMeanGradGrad
ExactMarginalLogLikelihood
LeaveOneOutPseudoLikelihood
VariationalELBO
PredictiveLogLikelihood
GammaRobustVariationalELBO
DeepApproximateMLL
AddedLossTerm
AddedLossTerm.loss()
InducingPointKernelAddedLossTerm
KLGaussianAddedLossTerm
KLQExponentialAddedLossTerm
metrics.mean_absolute_error()
metrics.mean_squared_error()
metrics.mean_standardized_log_loss()
metrics.negative_log_predictive_density()
metrics.quantile_coverage_error()
Interval
GreaterThan
Positive
LessThan
Distribution
Delta
MultivariateNormal
MultivariateNormal.__getitem__()
MultivariateNormal.add_jitter()
MultivariateNormal.confidence_region()
MultivariateNormal.expand()
MultivariateNormal.get_base_samples()
MultivariateNormal.log_prob()
MultivariateNormal.rsample()
MultivariateNormal.sample()
MultivariateNormal.to_data_independent_dist()
MultivariateNormal.unsqueeze()
MultitaskMultivariateNormal
MultitaskMultivariateNormal.__getitem__()
MultitaskMultivariateNormal.base_sample_shape
MultitaskMultivariateNormal.from_batch_mvn()
MultitaskMultivariateNormal.from_independent_mvns()
MultitaskMultivariateNormal.from_repeated_mvn()
MultitaskMultivariateNormal.to_data_independent_dist()
QExponential
MultivariateQExponential
MultivariateQExponential.__getitem__()
MultivariateQExponential.add_jitter()
MultivariateQExponential.confidence_region()
MultivariateQExponential.entropy()
MultivariateQExponential.expand()
MultivariateQExponential.get_base_samples()
MultivariateQExponential.log_prob()
MultivariateQExponential.rsample()
MultivariateQExponential.sample()
MultivariateQExponential.to_data_independent_dist()
MultivariateQExponential.to_data_uncorrelated_dist()
MultivariateQExponential.unsqueeze()
MultivariateQExponential.zero_mean_qep_samples()
MultitaskMultivariateQExponential
MultitaskMultivariateQExponential.__getitem__()
MultitaskMultivariateQExponential.base_sample_shape
MultitaskMultivariateQExponential.from_batch_qep()
MultitaskMultivariateQExponential.from_repeated_qep()
MultitaskMultivariateQExponential.from_uncorrelated_qeps()
MultitaskMultivariateQExponential.to_data_uncorrelated_dist()
Power
Prior
Prior.log_prob()
GammaPrior
HalfCauchyPrior
LKJCovariancePrior
MultivariateNormalPrior
MultivariateQExponentialPrior
NormalPrior
QExponentialPrior
SmoothedBoxPrior
_VariationalStrategy
VariationalStrategy
BatchDecoupledVariationalStrategy
CiqVariationalStrategy
NNVariationalStrategy
OrthogonallyDecoupledVariationalStrategy
UnwhitenedVariationalStrategy
GridInterpolationVariationalStrategy
LMCVariationalStrategy
IndependentMultitaskVariationalStrategy
MultitaskVariationalStrategy
_VariationalDistribution
CholeskyVariationalDistribution
DeltaVariationalDistribution
MeanFieldVariationalDistribution
NaturalVariationalDistribution
TrilNaturalVariationalDistribution
Settings and Beta Features
cg_tolerance
cholesky_jitter
cholesky_max_tries
ciq_samples
debug
detach_test_caches
deterministic_probes
eval_cg_tolerance
fast_computations
fast_computations.covar_root_decomposition
fast_computations.log_prob
fast_computations.solves
fast_pred_samples
fast_pred_var
lazily_evaluate_kernels
linalg_dtypes
max_cg_iterations
max_cholesky_size
max_eager_kernel_size
max_lanczos_quadrature_iterations
max_preconditioner_size
max_root_decomposition_size
memory_efficient
min_preconditioning_size
min_variance
minres_tolerance
num_contour_quadrature
num_gauss_hermite_locs
num_likelihood_samples
num_trace_samples
observation_nan_policy
preconditioner_tolerance
prior_mode
sgpr_diagonal_correction
skip_logdet_forward
skip_posterior_variances
terminate_cg_by_size
trace_mode
tridiagonal_jitter
use_keops
use_toeplitz
variational_cholesky_jitter
verbose_linalg
checkpoint_kernel
default_preconditioner
Advanced Package Reference
Module
Module.initialize()
Module.local_load_samples()
Module.named_added_loss_terms()
Module.named_priors()
Module.pyro_load_from_samples()
Module.pyro_sample_from_prior()
Module.register_parameter()
Module.register_prior()
Module.sample_from_prior()
add_diagonal()
add_jitter()
dsmm()
diagonalization()
inv_quad()
inv_quad_logdet()
pivoted_cholesky()
root_decomposition()
root_inv_decomposition()
solve()
sqrt_inv_matmul()
ScaleToBounds
choose_grid_size()
create_data_from_grid()
create_grid()
scale_to_bounds()
NNUtil
NNUtil.build_sequential_nn_idx()
NNUtil.find_nn_idx()
NNUtil.set_nn_idx()
NNUtil.to()
GaussHermiteQuadrature1D
GaussHermiteQuadrature1D.forward()
Deep QEP and Deep Sigma Point Processes are hierarchical models where each layer is a q-exponential process.
Standard Deep QEP regression
Multitask Deep QEP regression
DSPP regression
Multitask DSPP regression