| glm_families | GLM families |
| glm_fit | Runs multiple Fisher scoring steps |
| glm_fit_one_step | Runs one Fisher scoring step |
| glm_fit_one_step.tensorflow.tensor | Runs one Fisher Scoring step |
| glm_fit.tensorflow.tensor | Runs multiple Fisher scoring steps |
| initializer_blockwise | Blockwise Initializer |
| install_tfprobability | Installs TensorFlow Probability |
| layer_autoregressive | Masked Autoencoder for Distribution Estimation |
| layer_autoregressive_transform | An autoregressive normalizing flow layer, given a... |
| layer_categorical_mixture_of_one_hot_categorical | A OneHotCategorical mixture Keras layer from 'k * (1 + d)'... |
| layer_conv_1d_flipout | 1D convolution layer (e.g. temporal convolution) with Flipout |
| layer_conv_1d_reparameterization | 1D convolution layer (e.g. temporal convolution). |
| layer_conv_2d_flipout | 2D convolution layer (e.g. spatial convolution over images)... |
| layer_conv_2d_reparameterization | 2D convolution layer (e.g. spatial convolution over images) |
| layer_conv_3d_flipout | 3D convolution layer (e.g. spatial convolution over volumes)... |
| layer_conv_3d_reparameterization | 3D convolution layer (e.g. spatial convolution over volumes) |
| layer_dense_flipout | Densely-connected layer class with Flipout estimator. |
| layer_dense_local_reparameterization | Densely-connected layer class with local reparameterization... |
| layer_dense_reparameterization | Densely-connected layer class with reparameterization... |
| layer_dense_variational | Dense Variational Layer |
| layer_distribution_lambda | Keras layer enabling plumbing TFP distributions through Keras... |
| layer_independent_bernoulli | An Independent-Bernoulli Keras layer from prod(event_shape)... |
| layer_independent_logistic | An independent Logistic Keras layer. |
| layer_independent_normal | An independent Normal Keras layer. |
| layer_independent_poisson | An independent Poisson Keras layer. |
| layer_kl_divergence_add_loss | Pass-through layer that adds a KL divergence penalty to the... |
| layer_kl_divergence_regularizer | Regularizer that adds a KL divergence penalty to the model... |
| layer_mixture_logistic | A mixture distribution Keras layer, with independent logistic... |
| layer_mixture_normal | A mixture distribution Keras layer, with independent normal... |
| layer_mixture_same_family | A mixture (same-family) Keras layer. |
| layer_multivariate_normal_tri_l | A d-variate Multivariate Normal TriL Keras layer from... |
| layer_one_hot_categorical | A 'd'-variate OneHotCategorical Keras layer from 'd' params. |
| layer_variable | Variable Layer |
| layer_variational_gaussian_process | A Variational Gaussian Process Layer. |
| mcmc_dual_averaging_step_size_adaptation | Adapts the inner kernel's 'step_size' based on... |
| mcmc_effective_sample_size | Estimate a lower bound on effective sample size for each... |
| mcmc_hamiltonian_monte_carlo | Runs one step of Hamiltonian Monte Carlo. |
| mcmc_metropolis_adjusted_langevin_algorithm | Runs one step of Metropolis-adjusted Langevin algorithm. |
| mcmc_metropolis_hastings | Runs one step of the Metropolis-Hastings algorithm. |
| mcmc_no_u_turn_sampler | Runs one step of the No U-Turn Sampler |
| mcmc_potential_scale_reduction | Gelman and Rubin (1992)'s potential scale reduction for chain... |
| mcmc_random_walk_metropolis | Runs one step of the RWM algorithm with symmetric proposal. |
| mcmc_replica_exchange_mc | Runs one step of the Replica Exchange Monte Carlo |
| mcmc_sample_annealed_importance_chain | Runs annealed importance sampling (AIS) to estimate... |
| mcmc_sample_chain | Implements Markov chain Monte Carlo via repeated... |
| mcmc_sample_halton_sequence | Returns a sample from the 'dim' dimensional Halton sequence. |
| mcmc_simple_step_size_adaptation | Adapts the inner kernel's 'step_size' based on... |
| mcmc_slice_sampler | Runs one step of the slice sampler using a hit and run... |
| mcmc_transformed_transition_kernel | Applies a bijector to the MCMC's state space |
| mcmc_uncalibrated_hamiltonian_monte_carlo | Runs one step of Uncalibrated Hamiltonian Monte Carlo |
| mcmc_uncalibrated_langevin | Runs one step of Uncalibrated Langevin discretized diffusion. |
| mcmc_uncalibrated_random_walk | Generate proposal for the Random Walk Metropolis algorithm. |
| params_size_categorical_mixture_of_one_hot_categorical | number of 'params' needed to create a... |
| params_size_independent_bernoulli | number of 'params' needed to create an IndependentBernoulli... |
| params_size_independent_logistic | number of 'params' needed to create an IndependentLogistic... |
| params_size_independent_normal | number of 'params' needed to create an IndependentNormal... |
| params_size_independent_poisson | number of 'params' needed to create an IndependentPoisson... |
| params_size_mixture_logistic | number of 'params' needed to create a MixtureLogistic... |
| params_size_mixture_normal | number of 'params' needed to create a MixtureNormal... |
| params_size_mixture_same_family | number of 'params' needed to create a MixtureSameFamily... |
| params_size_multivariate_normal_tri_l | number of 'params' needed to create a MultivariateNormalTriL... |
| params_size_one_hot_categorical | number of 'params' needed to create a OneHotCategorical... |
| reexports | Objects exported from other packages |
| sts_additive_state_space_model | A state space model representing a sum of component state... |
| sts_autoregressive | Formal representation of an autoregressive model. |
| sts_autoregressive_state_space_model | State space model for an autoregressive process. |
| sts_build_factored_surrogate_posterior | Build a variational posterior that factors over model... |
| sts_build_factored_variational_loss | Build a loss function for variational inference in STS... |
| sts_constrained_seasonal_state_space_model | Seasonal state space model with effects constrained to sum to... |
| sts_decompose_by_component | Decompose an observed time series into contributions from... |
| sts_decompose_forecast_by_component | Decompose a forecast distribution into contributions from... |
| sts_dynamic_linear_regression | Formal representation of a dynamic linear regression model. |
| sts_dynamic_linear_regression_state_space_model | State space model for a dynamic linear regression from... |
| sts_fit_with_hmc | Draw posterior samples using Hamiltonian Monte Carlo (HMC) |
| sts_forecast | Construct predictive distribution over future observations |
| sts_linear_regression | Formal representation of a linear regression from provided... |
| sts_local_level | Formal representation of a local level model |
| sts_local_level_state_space_model | State space model for a local level |
| sts_local_linear_trend | Formal representation of a local linear trend model |
| sts_local_linear_trend_state_space_model | State space model for a local linear trend |
| sts_one_step_predictive | Compute one-step-ahead predictive distributions for all... |
| sts_sample_uniform_initial_state | Initialize from a uniform [-2, 2] distribution in... |
| sts_seasonal | Formal representation of a seasonal effect model. |
| sts_seasonal_state_space_model | State space model for a seasonal effect. |
| sts_semi_local_linear_trend | Formal representation of a semi-local linear trend model. |
| sts_semi_local_linear_trend_state_space_model | State space model for a semi-local linear trend. |
| sts_smooth_seasonal | Formal representation of a smooth seasonal effect model |
| sts_smooth_seasonal_state_space_model | State space model for a smooth seasonal effect |
| sts_sparse_linear_regression | Formal representation of a sparse linear regression. |
| sts_sum | Sum of structural time series components. |
| tfb_absolute_value | Computes'Y = g(X) = Abs(X)', element-wise |
| tfb_affine | Affine bijector |
| tfb_affine_linear_operator | ComputesY = g(X; shift, scale) = scale @ X + shift |
| tfb_affine_scalar | AffineScalar bijector (Deprecated) |
| tfb_ascending | Maps unconstrained R^n to R^n in ascending order. |
| tfb_batch_normalization | Computes'Y = g(X)' s.t. 'X = g^-1(Y) = (Y - mean(Y)) /... |
| tfb_blockwise | Bijector which applies a list of bijectors to blocks of a... |
| tfb_chain | Bijector which applies a sequence of bijectors |
| tfb_cholesky_outer_product | Computes'g(X) = X @ X.T' where 'X' is lower-triangular,... |
| tfb_cholesky_to_inv_cholesky | Maps the Cholesky factor of M to the Cholesky factor of... |
| tfb_correlation_cholesky | Maps unconstrained reals to Cholesky-space correlation... |
| tfb_cumsum | Computes the cumulative sum of a tensor along a specified... |
| tfb_discrete_cosine_transform | Computes'Y = g(X) = DCT(X)', where DCT type is indicated by... |
| tfb_exp | Computes'Y=g(X)=exp(X)' |
| tfb_expm1 | Computes'Y = g(X) = exp(X) - 1' |
| tfb_ffjord | Implements a continuous normalizing flow X->Y defined via an... |
| tfb_fill_scale_tri_l | Transforms unconstrained vectors to TriL matrices with... |
| tfb_fill_triangular | Transforms vectors to triangular |
| tfb_forward | Returns the forward Bijector evaluation, i.e., 'X = g(Y)'. |
| tfb_forward_log_det_jacobian | Returns the result of the forward evaluation of the log... |
| tfb_glow | Implements the Glow Bijector from Kingma & Dhariwal (2018). |
| tfb_gompertz_cdf | Compute Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1), the... |
| tfb_gumbel | Computes'Y = g(X) = exp(-exp(-(X - loc) / scale))' |
| tfb_gumbel_cdf | Compute 'Y = g(X) = exp(-exp(-(X - loc) / scale))', the... |
| tfb_identity | Computes'Y = g(X) = X' |
| tfb_inline | Bijector constructed from custom functions |
| tfb_inverse | Returns the inverse Bijector evaluation, i.e., 'X =... |
| tfb_inverse_log_det_jacobian | Returns the result of the inverse evaluation of the log... |
| tfb_invert | Bijector which inverts another Bijector |
| tfb_iterated_sigmoid_centered | Bijector which applies a Stick Breaking procedure. |
| tfb_kumaraswamy | Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X... |
| tfb_kumaraswamy_cdf | Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X... |
| tfb_lambert_w_tail | LambertWTail transformation for heavy-tail Lambert W x F... |
| tfb_masked_autoregressive_default_template | Masked Autoregressive Density Estimator |
| tfb_masked_autoregressive_flow | Affine MaskedAutoregressiveFlow bijector |
| tfb_masked_dense | Autoregressively masked dense layer |
| tfb_matrix_inverse_tri_l | Computes 'g(L) = inv(L)', where L is a lower-triangular... |
| tfb_matvec_lu | Matrix-vector multiply using LU decomposition |
| tfb_normal_cdf | Computes'Y = g(X) = NormalCDF(x)' |
| tfb_ordered | Bijector which maps a tensor x_k that has increasing elements... |
| tfb_pad | Pads a value to the 'event_shape' of a 'Tensor'. |
| tfb_permute | Permutes the rightmost dimension of a Tensor |
| tfb_power_transform | Computes'Y = g(X) = (1 + X * c)**(1 / c)', where 'X >= -1 /... |
| tfb_rational_quadratic_spline | A piecewise rational quadratic spline, as developed in Conor... |
| tfb_rayleigh_cdf | Compute Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0. |
| tfb_real_nvp | RealNVP affine coupling layer for vector-valued events |
| tfb_real_nvp_default_template | Build a scale-and-shift function using a multi-layer neural... |
| tfb_reciprocal | A Bijector that computes 'b(x) = 1. / x' |
| tfb_reshape | Reshapes the event_shape of a Tensor |
| tfb_scale | Compute Y = g(X; scale) = scale * X. |
| tfb_scale_matvec_diag | Compute Y = g(X; scale) = scale @ X |
| tfb_scale_matvec_linear_operator | Compute Y = g(X; scale) = scale @ X. |
| tfb_scale_matvec_lu | Matrix-vector multiply using LU decomposition. |
| tfb_scale_matvec_tri_l | Compute Y = g(X; scale) = scale @ X. |
| tfb_scale_tri_l | Transforms unconstrained vectors to TriL matrices with... |
| tfb_shift | Compute Y = g(X; shift) = X + shift. |
| tfb_shifted_gompertz_cdf | Compute 'Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate... |
| tfb_sigmoid | Computes'Y = g(X) = 1 / (1 + exp(-X))' |
| tfb_sinh | Bijector that computes 'Y = sinh(X)'. |
| tfb_sinh_arcsinh | Computes'Y = g(X) = Sinh( (Arcsinh(X) + skewness) *... |
| tfb_softmax_centered | Computes Y = g(X) = exp([X 0]) / sum(exp([X 0])) |
| tfb_softplus | Computes 'Y = g(X) = Log[1 + exp(X)]' |
| tfb_softsign | Computes Y = g(X) = X / (1 + |X|) |
| tfb_split | Split a 'Tensor' event along an axis into a list of... |
| tfb_square | Computes'g(X) = X^2'; X is a positive real number. |
| tfb_tanh | Computes 'Y = tanh(X)' |
| tfb_transform_diagonal | Applies a Bijector to the diagonal of a matrix |
| tfb_transpose | Computes'Y = g(X) = transpose_rightmost_dims(X,... |
| tfb_weibull | Computes'Y = g(X) = 1 - exp((-X / scale) ** concentration)'... |
| tfb_weibull_cdf | Compute Y = g(X) = 1 - exp((-X / scale) ** concentration), X... |
| tfd_autoregressive | Autoregressive distribution |
| tfd_batch_reshape | Batch-Reshaping distribution |
| tfd_bates | Bates distribution. |
| tfd_bernoulli | Bernoulli distribution |
| tfd_beta | Beta distribution |
| tfd_beta_binomial | Beta-Binomial compound distribution |
| tfd_binomial | Binomial distribution |
| tfd_blockwise | Blockwise distribution |
| tfd_categorical | Categorical distribution over integers |
| tfd_cauchy | Cauchy distribution with location 'loc' and scale 'scale' |
| tfd_cdf | Cumulative distribution function. Given random variable X,... |
| tfd_chi | Chi distribution |
| tfd_chi2 | Chi Square distribution |
| tfd_cholesky_lkj | The CholeskyLKJ distribution on cholesky factors of... |
| tfd_continuous_bernoulli | Continuous Bernoulli distribution. |
| tfd_covariance | Covariance. |
| tfd_cross_entropy | Computes the (Shannon) cross entropy. |
| tfd_deterministic | Scalar 'Deterministic' distribution on the real line |
| tfd_dirichlet | Dirichlet distribution |
| tfd_dirichlet_multinomial | Dirichlet-Multinomial compound distribution |
| tfd_doublesided_maxwell | Double-sided Maxwell distribution. |
| tfd_empirical | Empirical distribution |
| tfd_entropy | Shannon entropy in nats. |
| tfd_exp_gamma | ExpGamma distribution. |
| tfd_exp_inverse_gamma | ExpInverseGamma distribution. |
| tfd_exponential | Exponential distribution |
| tfd_exp_relaxed_one_hot_categorical | ExpRelaxedOneHotCategorical distribution with temperature and... |
| tfd_finite_discrete | The finite discrete distribution. |
| tfd_gamma | Gamma distribution |
| tfd_gamma_gamma | Gamma-Gamma distribution |
| tfd_gaussian_process | Marginal distribution of a Gaussian process at finitely many... |
| tfd_gaussian_process_regression_model | Posterior predictive distribution in a conjugate GP... |
| tfd_generalized_normal | The Generalized Normal distribution. |
| tfd_generalized_pareto | The Generalized Pareto distribution. |
| tfd_geometric | Geometric distribution |
| tfd_gumbel | Scalar Gumbel distribution with location 'loc' and 'scale'... |
| tfd_half_cauchy | Half-Cauchy distribution |
| tfd_half_normal | Half-Normal distribution with scale 'scale' |
| tfd_hidden_markov_model | Hidden Markov model distribution |
| tfd_horseshoe | Horseshoe distribution |
| tfd_independent | Independent distribution from batch of distributions |
| tfd_inverse_gamma | InverseGamma distribution |
| tfd_inverse_gaussian | Inverse Gaussian distribution |
| tfd_johnson_s_u | Johnson's SU-distribution. |
| tfd_joint_distribution_named | Joint distribution parameterized by named distribution-making... |
| tfd_joint_distribution_named_auto_batched | Joint distribution parameterized by named distribution-making... |
| tfd_joint_distribution_sequential | Joint distribution parameterized by distribution-making... |
| tfd_joint_distribution_sequential_auto_batched | Joint distribution parameterized by distribution-making... |
| tfd_kl_divergence | Computes the Kullback-Leibler divergence. |
| tfd_kumaraswamy | Kumaraswamy distribution |
| tfd_laplace | Laplace distribution with location 'loc' and 'scale'... |
| tfd_linear_gaussian_state_space_model | Observation distribution from a linear Gaussian state space... |
| tfd_lkj | LKJ distribution on correlation matrices |
| tfd_log_cdf | Log cumulative distribution function. |
| tfd_logistic | Logistic distribution with location 'loc' and 'scale'... |
| tfd_logit_normal | The Logit-Normal distribution |
| tfd_log_logistic | The log-logistic distribution. |
| tfd_log_normal | Log-normal distribution |
| tfd_log_prob | Log probability density/mass function. |
| tfd_log_survival_function | Log survival function. |
| tfd_mean | Mean. |
| tfd_mixture | Mixture distribution |
| tfd_mixture_same_family | Mixture (same-family) distribution |
| tfd_mode | Mode. |
| tfd_multinomial | Multinomial distribution |
| 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 | Multivariate normal distribution on 'R^k' |
| tfd_multivariate_normal_linear_operator | The multivariate normal distribution on 'R^k' |
| tfd_multivariate_normal_tri_l | The multivariate normal distribution on 'R^k' |
| tfd_multivariate_student_t_linear_operator | Multivariate Student's t-distribution on 'R^k' |
| tfd_negative_binomial | NegativeBinomial distribution |
| tfd_normal | Normal distribution with loc and scale parameters |
| tfd_one_hot_categorical | OneHotCategorical distribution |
| tfd_pareto | Pareto distribution |
| tfd_pert | Modified PERT distribution for modeling expert predictions. |
| tfd_pixel_cnn | The Pixel CNN++ distribution |
| tfd_plackett_luce | Plackett-Luce distribution over permutations. |
| tfd_poisson | Poisson distribution |
| tfd_poisson_log_normal_quadrature_compound | 'PoissonLogNormalQuadratureCompound' distribution |
| tfd_power_spherical | The Power Spherical distribution over unit vectors on... |
| tfd_prob | Probability density/mass function. |
| tfd_probit_bernoulli | ProbitBernoulli distribution. |
| tfd_quantile | Quantile function. Aka "inverse cdf" or "percent point... |
| tfd_quantized | Distribution representing the quantization 'Y = ceiling(X)' |
| tfd_relaxed_bernoulli | RelaxedBernoulli distribution with temperature and logits... |
| tfd_relaxed_one_hot_categorical | RelaxedOneHotCategorical distribution with temperature and... |
| tfd_sample | Generate samples of the specified shape. |
| tfd_sample_distribution | Sample distribution via independent draws. |
| tfd_sinh_arcsinh | The SinhArcsinh transformation of a distribution on (-inf,... |
| tfd_skellam | Skellam distribution. |
| tfd_spherical_uniform | The uniform distribution over unit vectors on 'S^{n-1}'. |
| tfd_stddev | Standard deviation. |
| tfd_student_t | Student's t-distribution |
| tfd_student_t_process | Marginal distribution of a Student's T process at finitely... |
| tfd_survival_function | Survival function. |
| tfd_transformed_distribution | A Transformed Distribution |
| tfd_triangular | Triangular distribution with 'low', 'high' and 'peak'... |
| tfd_truncated_cauchy | The Truncated Cauchy distribution. |
| tfd_truncated_normal | Truncated Normal distribution |
| tfd_uniform | Uniform distribution with 'low' and 'high' parameters |
| tfd_variance | Variance. |
| tfd_variational_gaussian_process | Posterior predictive of a variational Gaussian process |
| tfd_vector_deterministic | Vector Deterministic Distribution |
| tfd_vector_diffeomixture | VectorDiffeomixture distribution |
| tfd_vector_exponential_diag | The vectorization of the Exponential distribution on 'R^k' |
| tfd_vector_exponential_linear_operator | The vectorization of the Exponential distribution on 'R^k' |
| tfd_vector_laplace_diag | The vectorization of the Laplace distribution on 'R^k' |
| tfd_vector_laplace_linear_operator | The vectorization of the Laplace distribution on 'R^k' |
| tfd_vector_sinh_arcsinh_diag | The (diagonal) SinhArcsinh transformation of a distribution... |
| tfd_von_mises | The von Mises distribution over angles |
| tfd_von_mises_fisher | The von Mises-Fisher distribution over unit vectors on... |
| tfd_weibull | The Weibull distribution with 'concentration' and 'scale'... |
| tfd_wishart | The matrix Wishart distribution on positive definite matrices |
| tfd_wishart_linear_operator | The matrix Wishart distribution on positive definite matrices |
| tfd_wishart_tri_l | The matrix Wishart distribution parameterized with Cholesky... |
| tfd_zipf | Zipf distribution |
| tfp | Handle to the 'tensorflow_probability' module |
| tfp_version | TensorFlow Probability Version |
| vi_amari_alpha | The Amari-alpha Csiszar-function in log-space |
| vi_arithmetic_geometric | The Arithmetic-Geometric Csiszar-function in log-space |
| vi_chi_square | The chi-square Csiszar-function in log-space |
| vi_csiszar_vimco | Use VIMCO to lower the variance of the gradient of... |
| vi_dual_csiszar_function | Calculates the dual Csiszar-function in log-space |
| vi_fit_surrogate_posterior | Fit a surrogate posterior to a target (unnormalized) log... |
| vi_jeffreys | The Jeffreys Csiszar-function in log-space |
| vi_jensen_shannon | The Jensen-Shannon Csiszar-function in log-space |
| vi_kl_forward | The forward Kullback-Leibler Csiszar-function in log-space |
| vi_kl_reverse | The reverse Kullback-Leibler Csiszar-function in log-space |
| vi_log1p_abs | The log1p-abs Csiszar-function in log-space |
| vi_modified_gan | The Modified-GAN Csiszar-function in log-space |
| vi_monte_carlo_variational_loss | Monte-Carlo approximation of an f-Divergence variational loss |
| vi_pearson | The Pearson Csiszar-function in log-space |
| vi_squared_hellinger | The Squared-Hellinger Csiszar-function in log-space |
| vi_symmetrized_csiszar_function | Symmetrizes a Csiszar-function in log-space |
| vi_total_variation | The Total Variation Csiszar-function in log-space |
| vi_t_power | The T-Power Csiszar-function in log-space |
| vi_triangular | The Triangular Csiszar-function in log-space |
