Package: tfprobability 0.15.1.9000

Tomasz Kalinowski

tfprobability: Interface to 'TensorFlow Probability'

Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

Authors:Tomasz Kalinowski [ctb, cre], Sigrid Keydana [aut], Daniel Falbel [ctb], Kevin Kuo [ctb], RStudio [cph]

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tfprobability.pdf |tfprobability.html
tfprobability/json (API)
NEWS

# Install tfprobability in R:
install.packages('tfprobability', repos = c('https://rstudio.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/rstudio/tfprobability/issues

On CRAN:

298 exports 54 stars 3.72 score 33 dependencies 7 dependents

Last updated 2 years agofrom:917025f48b800e497470449c1589cb48f8ad1fe0

Exports:%>%glm_fitglm_fit_one_stepinitializer_blockwiseinstall_tfprobabilitylayer_autoregressivelayer_autoregressive_transformlayer_categorical_mixture_of_one_hot_categoricallayer_conv_1d_flipoutlayer_conv_1d_reparameterizationlayer_conv_2d_flipoutlayer_conv_2d_reparameterizationlayer_conv_3d_flipoutlayer_conv_3d_reparameterizationlayer_dense_flipoutlayer_dense_local_reparameterizationlayer_dense_reparameterizationlayer_dense_variationallayer_distribution_lambdalayer_independent_bernoullilayer_independent_logisticlayer_independent_normallayer_independent_poissonlayer_kl_divergence_add_losslayer_kl_divergence_regularizerlayer_mixture_logisticlayer_mixture_normallayer_mixture_same_familylayer_multivariate_normal_tri_llayer_one_hot_categoricallayer_variablelayer_variational_gaussian_processmcmc_dual_averaging_step_size_adaptationmcmc_effective_sample_sizemcmc_hamiltonian_monte_carlomcmc_metropolis_adjusted_langevin_algorithmmcmc_metropolis_hastingsmcmc_no_u_turn_samplermcmc_potential_scale_reductionmcmc_random_walk_metropolismcmc_replica_exchange_mcmcmc_sample_annealed_importance_chainmcmc_sample_chainmcmc_sample_halton_sequencemcmc_simple_step_size_adaptationmcmc_slice_samplermcmc_transformed_transition_kernelmcmc_uncalibrated_hamiltonian_monte_carlomcmc_uncalibrated_langevinmcmc_uncalibrated_random_walkparams_size_categorical_mixture_of_one_hot_categoricalparams_size_independent_bernoulliparams_size_independent_logisticparams_size_independent_normalparams_size_independent_poissonparams_size_mixture_logisticparams_size_mixture_normalparams_size_mixture_same_familyparams_size_multivariate_normal_tri_lparams_size_one_hot_categoricalshapests_additive_state_space_modelsts_autoregressivests_autoregressive_state_space_modelsts_build_factored_surrogate_posteriorsts_build_factored_variational_losssts_constrained_seasonal_state_space_modelsts_decompose_by_componentsts_decompose_forecast_by_componentsts_dynamic_linear_regressionsts_dynamic_linear_regression_state_space_modelsts_fit_with_hmcsts_forecaststs_linear_regressionsts_local_levelsts_local_level_state_space_modelsts_local_linear_trendsts_local_linear_trend_state_space_modelsts_one_step_predictivests_sample_uniform_initial_statests_seasonalsts_seasonal_state_space_modelsts_semi_local_linear_trendsts_semi_local_linear_trend_state_space_modelsts_smooth_seasonalsts_smooth_seasonal_state_space_modelsts_sparse_linear_regressionsts_sumtftf_configtfb_absolute_valuetfb_affinetfb_affine_linear_operatortfb_affine_scalartfb_ascendingtfb_batch_normalizationtfb_blockwisetfb_chaintfb_cholesky_outer_producttfb_cholesky_to_inv_choleskytfb_correlation_choleskytfb_cumsumtfb_discrete_cosine_transformtfb_exptfb_expm1tfb_ffjordtfb_fill_scale_tri_ltfb_fill_triangulartfb_forwardtfb_forward_log_det_jacobiantfb_glowtfb_gompertz_cdftfb_gumbeltfb_gumbel_cdftfb_identitytfb_inlinetfb_inversetfb_inverse_log_det_jacobiantfb_inverttfb_iterated_sigmoid_centeredtfb_kumaraswamytfb_kumaraswamy_cdftfb_lambert_w_tailtfb_masked_autoregressive_default_templatetfb_masked_autoregressive_flowtfb_masked_densetfb_matrix_inverse_tri_ltfb_matvec_lutfb_normal_cdftfb_orderedtfb_padtfb_permutetfb_power_transformtfb_rational_quadratic_splinetfb_rayleigh_cdftfb_real_nvptfb_real_nvp_default_templatetfb_reciprocaltfb_reshapetfb_scaletfb_scale_matvec_diagtfb_scale_matvec_linear_operatortfb_scale_matvec_lutfb_scale_matvec_tri_ltfb_scale_tri_ltfb_shifttfb_shifted_gompertz_cdftfb_sigmoidtfb_sinhtfb_sinh_arcsinhtfb_softmax_centeredtfb_softplustfb_softsigntfb_splittfb_squaretfb_tanhtfb_transform_diagonaltfb_transposetfb_weibulltfb_weibull_cdftfd_autoregressivetfd_batch_reshapetfd_batestfd_bernoullitfd_betatfd_beta_binomialtfd_binomialtfd_blockwisetfd_categoricaltfd_cauchytfd_cdftfd_chitfd_chi2tfd_cholesky_lkjtfd_continuous_bernoullitfd_covariancetfd_cross_entropytfd_deterministictfd_dirichlettfd_dirichlet_multinomialtfd_doublesided_maxwelltfd_empiricaltfd_entropytfd_exp_gammatfd_exp_inverse_gammatfd_exp_relaxed_one_hot_categoricaltfd_exponentialtfd_finite_discretetfd_gammatfd_gamma_gammatfd_gaussian_processtfd_gaussian_process_regression_modeltfd_generalized_normaltfd_generalized_paretotfd_geometrictfd_gumbeltfd_half_cauchytfd_half_normaltfd_hidden_markov_modeltfd_horseshoetfd_independenttfd_inverse_gammatfd_inverse_gaussiantfd_johnson_s_utfd_joint_distribution_namedtfd_joint_distribution_named_auto_batchedtfd_joint_distribution_sequentialtfd_joint_distribution_sequential_auto_batchedtfd_kl_divergencetfd_kumaraswamytfd_laplacetfd_linear_gaussian_state_space_modeltfd_lkjtfd_log_cdftfd_log_logistictfd_log_normaltfd_log_probtfd_log_survival_functiontfd_logistictfd_logit_normaltfd_meantfd_mixturetfd_mixture_same_familytfd_modetfd_multinomialtfd_multivariate_normal_diagtfd_multivariate_normal_diag_plus_low_ranktfd_multivariate_normal_full_covariancetfd_multivariate_normal_linear_operatortfd_multivariate_normal_tri_ltfd_multivariate_student_t_linear_operatortfd_negative_binomialtfd_normaltfd_one_hot_categoricaltfd_paretotfd_perttfd_pixel_cnntfd_plackett_lucetfd_poissontfd_poisson_log_normal_quadrature_compoundtfd_power_sphericaltfd_probtfd_probit_bernoullitfd_quantiletfd_quantizedtfd_relaxed_bernoullitfd_relaxed_one_hot_categoricaltfd_sampletfd_sample_distributiontfd_sinh_arcsinhtfd_skellamtfd_spherical_uniformtfd_stddevtfd_student_ttfd_student_t_processtfd_survival_functiontfd_transformed_distributiontfd_triangulartfd_truncated_cauchytfd_truncated_normaltfd_uniformtfd_variancetfd_variational_gaussian_processtfd_vector_deterministictfd_vector_diffeomixturetfd_vector_exponential_diagtfd_vector_exponential_linear_operatortfd_vector_laplace_diagtfd_vector_laplace_linear_operatortfd_vector_sinh_arcsinh_diagtfd_von_misestfd_von_mises_fishertfd_weibulltfd_wisharttfd_wishart_linear_operatortfd_wishart_tri_ltfd_zipftfptfp_versionvi_amari_alphavi_arithmetic_geometricvi_chi_squarevi_csiszar_vimcovi_dual_csiszar_functionvi_fit_surrogate_posteriorvi_jeffreysvi_jensen_shannonvi_kl_forwardvi_kl_reversevi_log1p_absvi_modified_ganvi_monte_carlo_variational_lossvi_pearsonvi_squared_hellingervi_symmetrized_csiszar_functionvi_t_powervi_total_variationvi_triangular

Dependencies:backportsbase64enccliconfiggenericsglueherejsonlitekeraslatticelifecyclemagrittrMatrixpngprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstidyselectvctrswhiskerwithryamlzeallot

Multi-level modeling with Hamiltonian Monte Carlo

Rendered fromhamiltonian_monte_carlo.Rmdusingknitr::rmarkdownon May 23 2024.

Last update: 2022-01-14
Started: 2019-06-29

Uncertainty estimates with layer_dense_variational

Rendered fromlayer_dense_variational.Rmdusingknitr::rmarkdownon May 23 2024.

Last update: 2022-01-15
Started: 2019-06-29

Readme and manuals

Help Manual

Help pageTopics
GLM familiesglm_families
Runs multiple Fisher scoring stepsglm_fit
Runs one Fisher scoring stepglm_fit_one_step
Runs one Fisher Scoring stepglm_fit_one_step.tensorflow.tensor
Runs multiple Fisher scoring stepsglm_fit.tensorflow.tensor
Blockwise Initializerinitializer_blockwise
Installs TensorFlow Probabilityinstall_tfprobability
Masked Autoencoder for Distribution Estimationlayer_autoregressive
An autoregressive normalizing flow layer, given a 'layer_autoregressive'.layer_autoregressive_transform
A OneHotCategorical mixture Keras layer from 'k * (1 + d)' params.layer_categorical_mixture_of_one_hot_categorical
1D convolution layer (e.g. temporal convolution) with Flipoutlayer_conv_1d_flipout
1D convolution layer (e.g. temporal convolution).layer_conv_1d_reparameterization
2D convolution layer (e.g. spatial convolution over images) with Flipoutlayer_conv_2d_flipout
2D convolution layer (e.g. spatial convolution over images)layer_conv_2d_reparameterization
3D convolution layer (e.g. spatial convolution over volumes) with Flipoutlayer_conv_3d_flipout
3D convolution layer (e.g. spatial convolution over volumes)layer_conv_3d_reparameterization
Densely-connected layer class with Flipout estimator.layer_dense_flipout
Densely-connected layer class with local reparameterization estimator.layer_dense_local_reparameterization
Densely-connected layer class with reparameterization estimator.layer_dense_reparameterization
Dense Variational Layerlayer_dense_variational
Keras layer enabling plumbing TFP distributions through Keras modelslayer_distribution_lambda
An Independent-Bernoulli Keras layer from prod(event_shape) paramslayer_independent_bernoulli
An independent Logistic Keras layer.layer_independent_logistic
An independent Normal Keras layer.layer_independent_normal
An independent Poisson Keras layer.layer_independent_poisson
Pass-through layer that adds a KL divergence penalty to the model losslayer_kl_divergence_add_loss
Regularizer that adds a KL divergence penalty to the model losslayer_kl_divergence_regularizer
A mixture distribution Keras layer, with independent logistic components.layer_mixture_logistic
A mixture distribution Keras layer, with independent normal components.layer_mixture_normal
A mixture (same-family) Keras layer.layer_mixture_same_family
A d-variate Multivariate Normal TriL Keras layer from 'd+d*(d+1)/ 2' paramslayer_multivariate_normal_tri_l
A 'd'-variate OneHotCategorical Keras layer from 'd' params.layer_one_hot_categorical
Variable Layerlayer_variable
A Variational Gaussian Process Layer.layer_variational_gaussian_process
Adapts the inner kernel's 'step_size' based on 'log_accept_prob'.mcmc_dual_averaging_step_size_adaptation
Estimate a lower bound on effective sample size for each independent chain.mcmc_effective_sample_size
Runs one step of Hamiltonian Monte Carlo.mcmc_hamiltonian_monte_carlo
Runs one step of Metropolis-adjusted Langevin algorithm.mcmc_metropolis_adjusted_langevin_algorithm
Runs one step of the Metropolis-Hastings algorithm.mcmc_metropolis_hastings
Runs one step of the No U-Turn Samplermcmc_no_u_turn_sampler
Gelman and Rubin (1992)'s potential scale reduction for chain convergence.mcmc_potential_scale_reduction
Runs one step of the RWM algorithm with symmetric proposal.mcmc_random_walk_metropolis
Runs one step of the Replica Exchange Monte Carlomcmc_replica_exchange_mc
Runs annealed importance sampling (AIS) to estimate normalizing constants.mcmc_sample_annealed_importance_chain
Implements Markov chain Monte Carlo via repeated 'TransitionKernel' steps.mcmc_sample_chain
Returns a sample from the 'dim' dimensional Halton sequence.mcmc_sample_halton_sequence
Adapts the inner kernel's 'step_size' based on 'log_accept_prob'.mcmc_simple_step_size_adaptation
Runs one step of the slice sampler using a hit and run approachmcmc_slice_sampler
Applies a bijector to the MCMC's state spacemcmc_transformed_transition_kernel
Runs one step of Uncalibrated Hamiltonian Monte Carlomcmc_uncalibrated_hamiltonian_monte_carlo
Runs one step of Uncalibrated Langevin discretized diffusion.mcmc_uncalibrated_langevin
Generate proposal for the Random Walk Metropolis algorithm.mcmc_uncalibrated_random_walk
number of 'params' needed to create a CategoricalMixtureOfOneHotCategorical distributionparams_size_categorical_mixture_of_one_hot_categorical
number of 'params' needed to create an IndependentBernoulli distributionparams_size_independent_bernoulli
number of 'params' needed to create an IndependentLogistic distributionparams_size_independent_logistic
number of 'params' needed to create an IndependentNormal distributionparams_size_independent_normal
number of 'params' needed to create an IndependentPoisson distributionparams_size_independent_poisson
number of 'params' needed to create a MixtureLogistic distributionparams_size_mixture_logistic
number of 'params' needed to create a MixtureNormal distributionparams_size_mixture_normal
number of 'params' needed to create a MixtureSameFamily distributionparams_size_mixture_same_family
number of 'params' needed to create a MultivariateNormalTriL distributionparams_size_multivariate_normal_tri_l
number of 'params' needed to create a OneHotCategorical distributionparams_size_one_hot_categorical
A state space model representing a sum of component state space models.sts_additive_state_space_model
Formal representation of an autoregressive model.sts_autoregressive
State space model for an autoregressive process.sts_autoregressive_state_space_model
Build a variational posterior that factors over model parameters.sts_build_factored_surrogate_posterior
Build a loss function for variational inference in STS models.sts_build_factored_variational_loss
Seasonal state space model with effects constrained to sum to zero.sts_constrained_seasonal_state_space_model
Decompose an observed time series into contributions from each component.sts_decompose_by_component
Decompose a forecast distribution into contributions from each component.sts_decompose_forecast_by_component
Formal representation of a dynamic linear regression model.sts_dynamic_linear_regression
State space model for a dynamic linear regression from provided covariates.sts_dynamic_linear_regression_state_space_model
Draw posterior samples using Hamiltonian Monte Carlo (HMC)sts_fit_with_hmc
Construct predictive distribution over future observationssts_forecast
Formal representation of a linear regression from provided covariates.sts_linear_regression
Formal representation of a local level modelsts_local_level
State space model for a local levelsts_local_level_state_space_model
Formal representation of a local linear trend modelsts_local_linear_trend
State space model for a local linear trendsts_local_linear_trend_state_space_model
Compute one-step-ahead predictive distributions for all timestepssts_one_step_predictive
Initialize from a uniform [-2, 2] distribution in unconstrained space.sts_sample_uniform_initial_state
Formal representation of a seasonal effect model.sts_seasonal
State space model for a seasonal effect.sts_seasonal_state_space_model
Formal representation of a semi-local linear trend model.sts_semi_local_linear_trend
State space model for a semi-local linear trend.sts_semi_local_linear_trend_state_space_model
Formal representation of a smooth seasonal effect modelsts_smooth_seasonal
State space model for a smooth seasonal effectsts_smooth_seasonal_state_space_model
Formal representation of a sparse linear regression.sts_sparse_linear_regression
Sum of structural time series components.sts_sum
Computes'Y = g(X) = Abs(X)', element-wisetfb_absolute_value
Affine bijectortfb_affine
ComputesY = g(X; shift, scale) = scale @ X + shifttfb_affine_linear_operator
Maps unconstrained R^n to R^n in ascending order.tfb_ascending
Computes'Y = g(X)' s.t. 'X = g^-1(Y) = (Y - mean(Y)) / std(Y)'tfb_batch_normalization
Bijector which applies a list of bijectors to blocks of a Tensortfb_blockwise
Bijector which applies a sequence of bijectorstfb_chain
Computes'g(X) = X @ X.T' where 'X' is lower-triangular, positive-diagonal matrixtfb_cholesky_outer_product
Maps the Cholesky factor of M to the Cholesky factor of 'M^{-1}'tfb_cholesky_to_inv_cholesky
Maps unconstrained reals to Cholesky-space correlation matrices.tfb_correlation_cholesky
Computes the cumulative sum of a tensor along a specified axis.tfb_cumsum
Computes'Y = g(X) = DCT(X)', where DCT type is indicated by the type argtfb_discrete_cosine_transform
Computes'Y=g(X)=exp(X)'tfb_exp
Computes'Y = g(X) = exp(X) - 1'tfb_expm1
Implements a continuous normalizing flow X->Y defined via an ODE.tfb_ffjord
Transforms unconstrained vectors to TriL matrices with positive diagonaltfb_fill_scale_tri_l
Transforms vectors to triangulartfb_fill_triangular
Returns the forward Bijector evaluation, i.e., 'X = g(Y)'.tfb_forward
Returns the result of the forward evaluation of the log determinant of the Jacobiantfb_forward_log_det_jacobian
Implements the Glow Bijector from Kingma & Dhariwal (2018).tfb_glow
Compute Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1), the Gompertz CDF.tfb_gompertz_cdf
Computes'Y = g(X) = exp(-exp(-(X - loc) / scale))'tfb_gumbel
Compute 'Y = g(X) = exp(-exp(-(X - loc) / scale))', the Gumbel CDF.tfb_gumbel_cdf
Computes'Y = g(X) = X'tfb_identity
Bijector constructed from custom functionstfb_inline
Returns the inverse Bijector evaluation, i.e., 'X = g^{-1}(Y)'.tfb_inverse
Returns the result of the inverse evaluation of the log determinant of the Jacobiantfb_inverse_log_det_jacobian
Bijector which inverts another Bijectortfb_invert
Bijector which applies a Stick Breaking procedure.tfb_iterated_sigmoid_centered
Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X in [0, 1]tfb_kumaraswamy
Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X in [0, 1]tfb_kumaraswamy_cdf
LambertWTail transformation for heavy-tail Lambert W x F random variables.tfb_lambert_w_tail
Masked Autoregressive Density Estimatortfb_masked_autoregressive_default_template
Affine MaskedAutoregressiveFlow bijectortfb_masked_autoregressive_flow
Autoregressively masked dense layertfb_masked_dense
Computes 'g(L) = inv(L)', where L is a lower-triangular matrixtfb_matrix_inverse_tri_l
Matrix-vector multiply using LU decompositiontfb_matvec_lu
Computes'Y = g(X) = NormalCDF(x)'tfb_normal_cdf
Bijector which maps a tensor x_k that has increasing elements in the last dimension to an unconstrained tensor y_ktfb_ordered
Pads a value to the 'event_shape' of a 'Tensor'.tfb_pad
Permutes the rightmost dimension of a Tensortfb_permute
Computes'Y = g(X) = (1 + X * c)**(1 / c)', where 'X >= -1 / c'tfb_power_transform
A piecewise rational quadratic spline, as developed in Conor et al.(2019).tfb_rational_quadratic_spline
Compute Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0.tfb_rayleigh_cdf
RealNVP affine coupling layer for vector-valued eventstfb_real_nvp
Build a scale-and-shift function using a multi-layer neural networktfb_real_nvp_default_template
A Bijector that computes 'b(x) = 1. / x'tfb_reciprocal
Reshapes the event_shape of a Tensortfb_reshape
Compute Y = g(X; scale) = scale * X.tfb_scale
Compute Y = g(X; scale) = scale @ Xtfb_scale_matvec_diag
Compute Y = g(X; scale) = scale @ X.tfb_scale_matvec_linear_operator
Matrix-vector multiply using LU decomposition.tfb_scale_matvec_lu
Compute Y = g(X; scale) = scale @ X.tfb_scale_matvec_tri_l
Transforms unconstrained vectors to TriL matrices with positive diagonaltfb_scale_tri_l
Compute Y = g(X; shift) = X + shift.tfb_shift
Compute 'Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X))'tfb_shifted_gompertz_cdf
Computes'Y = g(X) = 1 / (1 + exp(-X))'tfb_sigmoid
Bijector that computes 'Y = sinh(X)'.tfb_sinh
Computes'Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight )'tfb_sinh_arcsinh
Computes Y = g(X) = exp([X 0]) / sum(exp([X 0]))tfb_softmax_centered
Computes 'Y = g(X) = Log[1 + exp(X)]'tfb_softplus
Computes Y = g(X) = X / (1 + |X|)tfb_softsign
Split a 'Tensor' event along an axis into a list of 'Tensor's.tfb_split
Computes'g(X) = X^2'; X is a positive real number.tfb_square
Computes 'Y = tanh(X)'tfb_tanh
Applies a Bijector to the diagonal of a matrixtfb_transform_diagonal
Computes'Y = g(X) = transpose_rightmost_dims(X, rightmost_perm)'tfb_transpose
Computes'Y = g(X) = 1 - exp((-X / scale) ** concentration)' where X >= 0tfb_weibull
Compute Y = g(X) = 1 - exp((-X / scale) ** concentration), X >= 0.tfb_weibull_cdf
Autoregressive distributiontfd_autoregressive
Batch-Reshaping distributiontfd_batch_reshape
Bates distribution.tfd_bates
Bernoulli distributiontfd_bernoulli
Beta distributiontfd_beta
Beta-Binomial compound distributiontfd_beta_binomial
Binomial distributiontfd_binomial
Blockwise distributiontfd_blockwise
Categorical distribution over integerstfd_categorical
Cauchy distribution with location 'loc' and scale 'scale'tfd_cauchy
Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: 'cdf(x) := P[X <= x]'tfd_cdf
Chi distributiontfd_chi
Chi Square distributiontfd_chi2
The CholeskyLKJ distribution on cholesky factors of correlation matricestfd_cholesky_lkj
Continuous Bernoulli distribution.tfd_continuous_bernoulli
Covariance.tfd_covariance
Computes the (Shannon) cross entropy.tfd_cross_entropy
Scalar 'Deterministic' distribution on the real linetfd_deterministic
Dirichlet distributiontfd_dirichlet
Dirichlet-Multinomial compound distributiontfd_dirichlet_multinomial
Double-sided Maxwell distribution.tfd_doublesided_maxwell
Empirical distributiontfd_empirical
Shannon entropy in nats.tfd_entropy
ExpGamma distribution.tfd_exp_gamma
ExpInverseGamma distribution.tfd_exp_inverse_gamma
ExpRelaxedOneHotCategorical distribution with temperature and logits.tfd_exp_relaxed_one_hot_categorical
Exponential distributiontfd_exponential
The finite discrete distribution.tfd_finite_discrete
Gamma distributiontfd_gamma
Gamma-Gamma distributiontfd_gamma_gamma
Marginal distribution of a Gaussian process at finitely many points.tfd_gaussian_process
Posterior predictive distribution in a conjugate GP regression model.tfd_gaussian_process_regression_model
The Generalized Normal distribution.tfd_generalized_normal
The Generalized Pareto distribution.tfd_generalized_pareto
Geometric distributiontfd_geometric
Scalar Gumbel distribution with location 'loc' and 'scale' parameterstfd_gumbel
Half-Cauchy distributiontfd_half_cauchy
Half-Normal distribution with scale 'scale'tfd_half_normal
Hidden Markov model distributiontfd_hidden_markov_model
Horseshoe distributiontfd_horseshoe
Independent distribution from batch of distributionstfd_independent
InverseGamma distributiontfd_inverse_gamma
Inverse Gaussian distributiontfd_inverse_gaussian
Johnson's SU-distribution.tfd_johnson_s_u
Joint distribution parameterized by named distribution-making functions.tfd_joint_distribution_named
Joint distribution parameterized by named distribution-making functions.tfd_joint_distribution_named_auto_batched
Joint distribution parameterized by distribution-making functionstfd_joint_distribution_sequential
Joint distribution parameterized by distribution-making functions.tfd_joint_distribution_sequential_auto_batched
Computes the Kullback-Leibler divergence.tfd_kl_divergence
Kumaraswamy distributiontfd_kumaraswamy
Laplace distribution with location 'loc' and 'scale' parameterstfd_laplace
Observation distribution from a linear Gaussian state space modeltfd_linear_gaussian_state_space_model
LKJ distribution on correlation matricestfd_lkj
Log cumulative distribution function.tfd_log_cdf
The log-logistic distribution.tfd_log_logistic
Log-normal distributiontfd_log_normal
Log probability density/mass function.tfd_log_prob
Log survival function.tfd_log_survival_function
Logistic distribution with location 'loc' and 'scale' parameterstfd_logistic
The Logit-Normal distributiontfd_logit_normal
Mean.tfd_mean
Mixture distributiontfd_mixture
Mixture (same-family) distributiontfd_mixture_same_family
Mode.tfd_mode
Multinomial distributiontfd_multinomial
Multivariate normal distribution on 'R^k'tfd_multivariate_normal_diag
Multivariate normal distribution on 'R^k'tfd_multivariate_normal_diag_plus_low_rank
Multivariate normal distribution on 'R^k'tfd_multivariate_normal_full_covariance
The multivariate normal distribution on 'R^k'tfd_multivariate_normal_linear_operator
The multivariate normal distribution on 'R^k'tfd_multivariate_normal_tri_l
Multivariate Student's t-distribution on 'R^k'tfd_multivariate_student_t_linear_operator
NegativeBinomial distributiontfd_negative_binomial
Normal distribution with loc and scale parameterstfd_normal
OneHotCategorical distributiontfd_one_hot_categorical
Pareto distributiontfd_pareto
Modified PERT distribution for modeling expert predictions.tfd_pert
The Pixel CNN++ distributiontfd_pixel_cnn
Plackett-Luce distribution over permutations.tfd_plackett_luce
Poisson distributiontfd_poisson
'PoissonLogNormalQuadratureCompound' distributiontfd_poisson_log_normal_quadrature_compound
The Power Spherical distribution over unit vectors on 'S^{n-1}'.tfd_power_spherical
Probability density/mass function.tfd_prob
ProbitBernoulli distribution.tfd_probit_bernoulli
Quantile function. Aka "inverse cdf" or "percent point function".tfd_quantile
Distribution representing the quantization 'Y = ceiling(X)'tfd_quantized
RelaxedBernoulli distribution with temperature and logits parameterstfd_relaxed_bernoulli
RelaxedOneHotCategorical distribution with temperature and logitstfd_relaxed_one_hot_categorical
Generate samples of the specified shape.tfd_sample
Sample distribution via independent draws.tfd_sample_distribution
The SinhArcsinh transformation of a distribution on (-inf, inf)tfd_sinh_arcsinh
Skellam distribution.tfd_skellam
The uniform distribution over unit vectors on 'S^{n-1}'.tfd_spherical_uniform
Standard deviation.tfd_stddev
Student's t-distributiontfd_student_t
Marginal distribution of a Student's T process at finitely many pointstfd_student_t_process
Survival function.tfd_survival_function
A Transformed Distributiontfd_transformed_distribution
Triangular distribution with 'low', 'high' and 'peak' parameterstfd_triangular
The Truncated Cauchy distribution.tfd_truncated_cauchy
Truncated Normal distributiontfd_truncated_normal
Uniform distribution with 'low' and 'high' parameterstfd_uniform
Variance.tfd_variance
Posterior predictive of a variational Gaussian processtfd_variational_gaussian_process
Vector Deterministic Distributiontfd_vector_deterministic
VectorDiffeomixture distributiontfd_vector_diffeomixture
The vectorization of the Exponential distribution on 'R^k'tfd_vector_exponential_diag
The vectorization of the Exponential distribution on 'R^k'tfd_vector_exponential_linear_operator
The vectorization of the Laplace distribution on 'R^k'tfd_vector_laplace_diag
The vectorization of the Laplace distribution on 'R^k'tfd_vector_laplace_linear_operator
The (diagonal) SinhArcsinh transformation of a distribution on 'R^k'tfd_vector_sinh_arcsinh_diag
The von Mises distribution over anglestfd_von_mises
The von Mises-Fisher distribution over unit vectors on 'S^{n-1}'tfd_von_mises_fisher
The Weibull distribution with 'concentration' and 'scale' parameters.tfd_weibull
The matrix Wishart distribution on positive definite matricestfd_wishart
The matrix Wishart distribution on positive definite matricestfd_wishart_linear_operator
The matrix Wishart distribution parameterized with Cholesky factors.tfd_wishart_tri_l
Zipf distributiontfd_zipf
Handle to the 'tensorflow_probability' moduletfp
TensorFlow Probability Versiontfp_version
The Amari-alpha Csiszar-function in log-spacevi_amari_alpha
The Arithmetic-Geometric Csiszar-function in log-spacevi_arithmetic_geometric
The chi-square Csiszar-function in log-spacevi_chi_square
Use VIMCO to lower the variance of the gradient of csiszar_function(Avg(logu))vi_csiszar_vimco
Calculates the dual Csiszar-function in log-spacevi_dual_csiszar_function
Fit a surrogate posterior to a target (unnormalized) log densityvi_fit_surrogate_posterior
The Jeffreys Csiszar-function in log-spacevi_jeffreys
The Jensen-Shannon Csiszar-function in log-spacevi_jensen_shannon
The forward Kullback-Leibler Csiszar-function in log-spacevi_kl_forward
The reverse Kullback-Leibler Csiszar-function in log-spacevi_kl_reverse
The log1p-abs Csiszar-function in log-spacevi_log1p_abs
The Modified-GAN Csiszar-function in log-spacevi_modified_gan
Monte-Carlo approximation of an f-Divergence variational lossvi_monte_carlo_variational_loss
The Pearson Csiszar-function in log-spacevi_pearson
The Squared-Hellinger Csiszar-function in log-spacevi_squared_hellinger
Symmetrizes a Csiszar-function in log-spacevi_symmetrized_csiszar_function
The T-Power Csiszar-function in log-spacevi_t_power
The Total Variation Csiszar-function in log-spacevi_total_variation
The Triangular Csiszar-function in log-spacevi_triangular