The vector exponential distribution is defined over a subset of R^k, and parameterized by a (batch of) length-k loc vector and a (batch of) k x k scale matrix: covariance = scale @ scale.T, where @ denotes matrix-multiplication.

  loc = NULL,
  scale_diag = NULL,
  scale_identity_multiplier = NULL,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = "VectorExponentialDiag"



Floating-point Tensor. If this is set to NULL, loc is implicitly 0. When specified, may have shape [B1, ..., Bb, k] where b >= 0 and k is the event size.


Non-zero, floating-point Tensor representing a diagonal matrix added to scale. May have shape [B1, ..., Bb, k], b >= 0, and characterizes b-batches of k x k diagonal matrices added to scale. When both scale_identity_multiplier and scale_diag are NULL then scale is the Identity.


Non-zero, floating-point Tensor representing a scaled-identity-matrix added to scale. May have shape [B1, ..., Bb], b >= 0, and characterizes b-batches of scaled k x k identity matrices added to scale. When both scale_identity_multiplier and scale_diag are NULL then scale is the Identity.


Logical, default FALSE. When TRUE distribution parameters are checked for validity despite possibly degrading runtime performance. When FALSE invalid inputs may silently render incorrect outputs. Default value: FALSE.


Logical, default TRUE. When TRUE, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When FALSE, an exception is raised if one or more of the statistic's batch members are undefined.


name prefixed to Ops created by this class.


a distribution instance.


Mathematical Details The probability density function (pdf) is defined over the image of the scale matrix + loc, applied to the positive half-space: Supp = {loc + scale @ x : x in R^k, x_1 > 0, ..., x_k > 0}. On this set,

pdf(y; loc, scale) = exp(-||x||_1) / Z,  for y in Supp
x = inv(scale) @ (y - loc),
Z = |det(scale)|,


  • loc is a vector in R^k,

  • scale is a linear operator in R^{k x k}, cov = scale @ scale.T,

  • Z denotes the normalization constant, and,

  • ||x||_1 denotes the l1 norm of x, sum_i |x_i|. The VectorExponential distribution is a member of the location-scale family, i.e., it can be constructed as,

X = (X_1, ..., X_k), each X_i ~ Exponential(rate=1)
Y = (Y_1, ...,Y_k) = scale @ X + loc

About VectorExponential and Vector distributions in TensorFlow.

The VectorExponential is a non-standard distribution that has useful properties. The marginals Y_1, ..., Y_k are not Exponential random variables, due to the fact that the sum of Exponential random variables is not Exponential. Instead, Y is a vector whose components are linear combinations of Exponential random variables. Thus, Y lives in the vector space generated by vectors of Exponential distributions. This allows the user to decide the mean and covariance (by setting loc and scale), while preserving some properties of the Exponential distribution. In particular, the tails of Y_i will be (up to polynomial factors) exponentially decaying. To see this last statement, note that the pdf of Y_i is the convolution of the pdf of k independent Exponential random variables. One can then show by induction that distributions with exponential (up to polynomial factors) tails are closed under convolution.

The batch_shape is the broadcast shape between loc and scale arguments. The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc (if provided) must broadcast with this. Recall that covariance = 2 * scale @ scale.T. Additional leading dimensions (if any) will index batches. If both scale_diag and scale_identity_multiplier are NULL, then scale is the Identity matrix.

See also

For usage examples see e.g. tfd_sample(), tfd_log_prob(), tfd_mean().

Other distributions: tfd_autoregressive(), tfd_batch_reshape(), tfd_bates(), tfd_bernoulli(), tfd_beta_binomial(), tfd_beta(), tfd_binomial(), tfd_categorical(), tfd_cauchy(), tfd_chi2(), tfd_chi(), tfd_cholesky_lkj(), tfd_continuous_bernoulli(), tfd_deterministic(), tfd_dirichlet_multinomial(), tfd_dirichlet(), tfd_empirical(), tfd_exp_gamma(), tfd_exp_inverse_gamma(), tfd_exponential(), tfd_gamma_gamma(), tfd_gamma(), tfd_gaussian_process_regression_model(), tfd_gaussian_process(), tfd_generalized_normal(), tfd_geometric(), tfd_gumbel(), tfd_half_cauchy(), tfd_half_normal(), tfd_hidden_markov_model(), tfd_horseshoe(), tfd_independent(), tfd_inverse_gamma(), tfd_inverse_gaussian(), tfd_johnson_s_u(), tfd_joint_distribution_named_auto_batched(), tfd_joint_distribution_named(), tfd_joint_distribution_sequential_auto_batched(), tfd_joint_distribution_sequential(), tfd_kumaraswamy(), tfd_laplace(), tfd_linear_gaussian_state_space_model(), tfd_lkj(), tfd_log_logistic(), tfd_log_normal(), tfd_logistic(), tfd_mixture_same_family(), tfd_mixture(), tfd_multinomial(), tfd_multivariate_normal_diag_plus_low_rank(), tfd_multivariate_normal_diag(), tfd_multivariate_normal_full_covariance(), tfd_multivariate_normal_linear_operator(), tfd_multivariate_normal_tri_l(), tfd_multivariate_student_t_linear_operator(), tfd_negative_binomial(), tfd_normal(), tfd_one_hot_categorical(), tfd_pareto(), tfd_pixel_cnn(), tfd_poisson_log_normal_quadrature_compound(), tfd_poisson(), tfd_power_spherical(), tfd_probit_bernoulli(), tfd_quantized(), tfd_relaxed_bernoulli(), tfd_relaxed_one_hot_categorical(), tfd_sample_distribution(), tfd_sinh_arcsinh(), tfd_skellam(), tfd_spherical_uniform(), tfd_student_t_process(), tfd_student_t(), tfd_transformed_distribution(), tfd_triangular(), tfd_truncated_cauchy(), tfd_truncated_normal(), tfd_uniform(), tfd_variational_gaussian_process(), tfd_vector_diffeomixture(), tfd_vector_exponential_linear_operator(), tfd_vector_laplace_diag(), tfd_vector_laplace_linear_operator(), tfd_vector_sinh_arcsinh_diag(), tfd_von_mises_fisher(), tfd_von_mises(), tfd_weibull(), tfd_wishart_linear_operator(), tfd_wishart_tri_l(), tfd_wishart(), tfd_zipf()