# Interface to 'TensorFlow Probability'

## Documentation for package ‘tfprobability’ version 0.15.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

G I L M P S T V

## -- G --

glm_families | GLM families
glm_fit | Runs multiple Fisher scoring steps
glm_fit.tensorflow.tensor | 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

## -- I --

initializer_blockwise | Blockwise Initializer
install_tfprobability | Installs TensorFlow Probability

## -- L --

layer_autoregressive | Masked Autoencoder for Distribution Estimation
layer_autoregressive_transform | An autoregressive normalizing flow layer, given a 'layer_autoregressive'.
layer_categorical_mixture_of_one_hot_categorical | A OneHotCategorical mixture Keras layer from 'k * (1 + d)' params.
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) with Flipout
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) with Flipout
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 estimator.
layer_dense_reparameterization | Densely-connected layer class with reparameterization estimator.
layer_dense_variational | Dense Variational Layer
layer_distribution_lambda | Keras layer enabling plumbing TFP distributions through Keras models
layer_independent_bernoulli | An Independent-Bernoulli Keras layer from prod(event_shape) params
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 model loss
layer_kl_divergence_regularizer | Regularizer that adds a KL divergence penalty to the model loss
layer_mixture_logistic | A mixture distribution Keras layer, with independent logistic components.
layer_mixture_normal | A mixture distribution Keras layer, with independent normal components.
layer_mixture_same_family | A mixture (same-family) Keras layer.
layer_multivariate_normal_tri_l | A d-variate Multivariate Normal TriL Keras layer from 'd+d*(d+1)/ 2' params
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.

## -- M --

mcmc_dual_averaging_step_size_adaptation | Adapts the inner kernel's 'step_size' based on 'log_accept_prob'.
mcmc_effective_sample_size | Estimate a lower bound on effective sample size for each independent chain.
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 convergence.
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 normalizing constants.
mcmc_sample_chain | Implements Markov chain Monte Carlo via repeated 'TransitionKernel' steps.
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 'log_accept_prob'.
mcmc_slice_sampler | Runs one step of the slice sampler using a hit and run approach
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.

## -- P --

params_size_categorical_mixture_of_one_hot_categorical | number of 'params' needed to create a CategoricalMixtureOfOneHotCategorical distribution
params_size_independent_bernoulli | number of 'params' needed to create an IndependentBernoulli distribution
params_size_independent_logistic | number of 'params' needed to create an IndependentLogistic distribution
params_size_independent_normal | number of 'params' needed to create an IndependentNormal distribution
params_size_independent_poisson | number of 'params' needed to create an IndependentPoisson distribution
params_size_mixture_logistic | number of 'params' needed to create a MixtureLogistic distribution
params_size_mixture_normal | number of 'params' needed to create a MixtureNormal distribution
params_size_mixture_same_family | number of 'params' needed to create a MixtureSameFamily distribution
params_size_multivariate_normal_tri_l | number of 'params' needed to create a MultivariateNormalTriL distribution
params_size_one_hot_categorical | number of 'params' needed to create a OneHotCategorical distribution

## -- S --

sts_additive_state_space_model | A state space model representing a sum of component state space models.
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 parameters.
sts_build_factored_variational_loss | Build a loss function for variational inference in STS models.
sts_constrained_seasonal_state_space_model | Seasonal state space model with effects constrained to sum to zero.
sts_decompose_by_component | Decompose an observed time series into contributions from each component.
sts_decompose_forecast_by_component | Decompose a forecast distribution into contributions from each component.
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 provided covariates.
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 covariates.
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 timesteps
sts_sample_uniform_initial_state | Initialize from a uniform [-2, 2] distribution in unconstrained space.
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.

## -- T --

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_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)) / std(Y)'
tfb_blockwise | Bijector which applies a list of bijectors to blocks of a Tensor
tfb_chain | Bijector which applies a sequence of bijectors
tfb_cholesky_outer_product | Computes'g(X) = X @ X.T' where 'X' is lower-triangular, positive-diagonal matrix
tfb_cholesky_to_inv_cholesky | Maps the Cholesky factor of M to the Cholesky factor of 'M^{-1}'
tfb_correlation_cholesky | Maps unconstrained reals to Cholesky-space correlation matrices.
tfb_cumsum | Computes the cumulative sum of a tensor along a specified axis.
tfb_discrete_cosine_transform | Computes'Y = g(X) = DCT(X)', where DCT type is indicated by the type arg
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 ODE.
tfb_fill_scale_tri_l | Transforms unconstrained vectors to TriL matrices with positive diagonal
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 determinant of the Jacobian
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 Gompertz CDF.
tfb_gumbel | Computes'Y = g(X) = exp(-exp(-(X - loc) / scale))'
tfb_gumbel_cdf | Compute 'Y = g(X) = exp(-exp(-(X - loc) / scale))', the Gumbel CDF.
tfb_identity | Computes'Y = g(X) = X'
tfb_inline | Bijector constructed from custom functions
tfb_inverse | Returns the inverse Bijector evaluation, i.e., 'X = g^{-1}(Y)'.
tfb_inverse_log_det_jacobian | Returns the result of the inverse evaluation of the log determinant of the Jacobian
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 in [0, 1]
tfb_kumaraswamy_cdf | Computes'Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)', with X in [0, 1]
tfb_lambert_w_tail | LambertWTail transformation for heavy-tail Lambert W x F random variables.
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 matrix
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 in the last dimension to an unconstrained tensor y_k
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 / c'
tfb_rational_quadratic_spline | A piecewise rational quadratic spline, as developed in Conor et al.(2019).
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 network
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 positive diagonal
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 * X))'
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) * tailweight )'
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 'Tensor's.
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, rightmost_perm)'
tfb_weibull | Computes'Y = g(X) = 1 - exp((-X / scale) ** concentration)' where X >= 0
tfb_weibull_cdf | Compute Y = g(X) = 1 - exp((-X / scale) ** concentration), X >= 0.
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, the cumulative distribution function cdf is: 'cdf(x) := P[X <= x]'
tfd_chi | Chi distribution
tfd_chi2 | Chi Square distribution
tfd_cholesky_lkj | The CholeskyLKJ distribution on cholesky factors of correlation matrices
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_exponential | Exponential distribution
tfd_exp_gamma | ExpGamma distribution.
tfd_exp_inverse_gamma | ExpInverseGamma distribution.
tfd_exp_relaxed_one_hot_categorical | ExpRelaxedOneHotCategorical distribution with temperature and logits.
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 points.
tfd_gaussian_process_regression_model | Posterior predictive distribution in a conjugate GP regression model.
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' parameters
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 functions.
tfd_joint_distribution_named_auto_batched | Joint distribution parameterized by named distribution-making functions.
tfd_joint_distribution_sequential | Joint distribution parameterized by distribution-making functions
tfd_joint_distribution_sequential_auto_batched | Joint distribution parameterized by distribution-making functions.
tfd_kl_divergence | Computes the Kullback-Leibler divergence.
tfd_kumaraswamy | Kumaraswamy distribution
tfd_laplace | Laplace distribution with location 'loc' and 'scale' parameters
tfd_linear_gaussian_state_space_model | Observation distribution from a linear Gaussian state space model
tfd_lkj | LKJ distribution on correlation matrices
tfd_logistic | Logistic distribution with location 'loc' and 'scale' parameters
tfd_logit_normal | The Logit-Normal distribution
tfd_log_cdf | Log cumulative distribution function.
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 'S^{n-1}'.
tfd_prob | Probability density/mass function.
tfd_probit_bernoulli | ProbitBernoulli distribution.
tfd_quantile | Quantile function. Aka "inverse cdf" or "percent point function".
tfd_quantized | Distribution representing the quantization 'Y = ceiling(X)'
tfd_relaxed_bernoulli | RelaxedBernoulli distribution with temperature and logits parameters
tfd_relaxed_one_hot_categorical | RelaxedOneHotCategorical distribution with temperature and logits
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, 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 many points
tfd_survival_function | Survival function.
tfd_transformed_distribution | A Transformed Distribution
tfd_triangular | Triangular distribution with 'low', 'high' and 'peak' parameters
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 on 'R^k'
tfd_von_mises | The von Mises distribution over angles
tfd_von_mises_fisher | The von Mises-Fisher distribution over unit vectors on 'S^{n-1}'
tfd_weibull | The Weibull distribution with 'concentration' and 'scale' parameters.
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 factors.
tfd_zipf | Zipf distribution
tfp | Handle to the 'tensorflow_probability' module
tfp_version | TensorFlow Probability Version

## -- V --

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 csiszar_function(Avg(logu))
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 density
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_triangular | The Triangular Csiszar-function in log-space
vi_t_power | The T-Power Csiszar-function in log-space