Function reference • tfprobability

Distributions
`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_categorical()`	Categorical distribution over integers
`tfd_cauchy()`	Cauchy distribution with location `loc` and scale `scale`
`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_deterministic()`	Scalar `Deterministic` distribution on the real line
`tfd_dirichlet()`	Dirichlet distribution
`tfd_dirichlet_multinomial()`	Dirichlet-Multinomial compound distribution
`tfd_empirical()`	Empirical distribution
`tfd_exp_gamma()`	ExpGamma distribution.
`tfd_exp_inverse_gamma()`	ExpInverseGamma distribution.
`tfd_exponential()`	Exponential 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_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_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_log_logistic()`	The log-logistic distribution.
`tfd_log_normal()` `tfd_log_normal()`	Log-normal distribution
`tfd_logistic()`	Logistic distribution with location `loc` and `scale` parameters
`tfd_mixture()`	Mixture distribution
`tfd_mixture_same_family()`	Mixture (same-family) distribution
`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_pixel_cnn()`	The Pixel CNN++ distribution
`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_probit_bernoulli()`	ProbitBernoulli distribution.
`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_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_student_t()`	Student's t-distribution
`tfd_student_t_process()`	Marginal distribution of a Student's T process at finitely many points
`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_variational_gaussian_process()`	Posterior predictive of a variational Gaussian process
`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
Distribution methods
`tfd_cdf()`	Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: `cdf(x) := P[X <= x]`
`tfd_covariance()`	Covariance.
`tfd_cross_entropy()`	Computes the (Shannon) cross entropy.
`tfd_entropy()`	Shannon entropy in nats.
`tfd_kl_divergence()`	Computes the Kullback--Leibler divergence.
`tfd_log_cdf()`	Log cumulative distribution function.
`tfd_log_prob()`	Log probability density/mass function.
`tfd_log_survival_function()`	Log survival function.
`tfd_mean()`	Mean.
`tfd_mode()`	Mode.
`tfd_prob()`	Probability density/mass function.
`tfd_quantile()`	Quantile function. Aka "inverse cdf" or "percent point function".
`tfd_sample()`	Generate samples of the specified shape.
`tfd_stddev()`	Standard deviation.
`tfd_survival_function()`	Survival function.
`tfd_variance()`	Variance.
Keras layers: Distribution layers
`layer_categorical_mixture_of_one_hot_categorical()`	A OneHotCategorical mixture Keras layer from `k * (1 + d)` params.
`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.
Keras layers: Other
`layer_autoregressive()`	Masked Autoencoder for Distribution Estimation
`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_variable()`	Variable Layer
Bijectors
`tfb_absolute_value()`	Computes`Y = g(X) = Abs(X)`, element-wise
`tfb_affine()`	Affine bijector
`tfb_affine_linear_operator()`	Computes`Y = g(X; shift, scale) = scale @ X + shift`
`tfb_affine_scalar()`	AffineScalar bijector
`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_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_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`.
Bijector methods
`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_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
Variational inference
`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
MCMC kernels
`mcmc_dual_averaging_step_size_adaptation()`	Adapts the inner kernel's `step_size` based on `log_accept_prob`.
`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_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_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.
MCMC functions
`mcmc_effective_sample_size()`	Estimate a lower bound on effective sample size for each independent chain.
`mcmc_potential_scale_reduction()`	Gelman and Rubin (1992)'s potential scale reduction for chain convergence.
`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.
Structural time series models
`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_constrained_seasonal_state_space_model()`	Seasonal state space model with effects constrained to sum to zero.
`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_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_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.
Structural time series modeling functions
`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_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_fit_with_hmc()`	Draw posterior samples using Hamiltonian Monte Carlo (HMC)
`sts_forecast()`	Construct predictive distribution over future observations
`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.
Generalized Linear Models
`glm_families`	GLM families
`glm_fit(<tensorflow.tensor>)`	Runs multiple Fisher scoring steps
`glm_fit_one_step(<tensorflow.tensor>)`	Runs one Fisher Scoring step

Reference

Distributions

Distribution methods

Keras layers: Distribution layers

Keras layers: Other

Bijectors

Bijector methods

Variational inference

MCMC kernels

MCMC functions

Structural time series models

Structural time series modeling functions

Generalized Linear Models