| 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 |
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