The vector laplace distribution is defined over R^k, and parameterized by a (batch of) length-k loc vector (the means) and a (batch of) k x k scale matrix: covariance = 2 * scale @ scale.T, where @ denotes matrix-multiplication.

  loc = NULL,
  scale = NULL,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = "VectorLaplaceLinearOperator"



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.


Instance of LinearOperator with same dtype as loc and shape [B1, ..., Bb, k, k].


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,

pdf(x; loc, scale) = exp(-||y||_1) / Z,
y = inv(scale) @ (x - loc),
Z = 2**k |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,

  • ||y||_1 denotes the l1 norm of y, `sum_i |y_i|.

The VectorLaplace distribution is a member of the location-scale family, i.e., it can be constructed as,

X = (X_1, ..., X_k), each X_i ~ Laplace(loc=0, scale=1)
Y = (Y_1, ...,Y_k) = scale @ X + loc

About VectorLaplace and Vector distributions in TensorFlow

The VectorLaplace is a non-standard distribution that has useful properties. The marginals Y_1, ..., Y_k are not Laplace random variables, due to the fact that the sum of Laplace random variables is not Laplace. Instead, Y is a vector whose components are linear combinations of Laplace random variables. Thus, Y lives in the vector space generated by vectors of Laplace distributions. This allows the user to decide the mean and covariance (by setting loc and scale), while preserving some properties of the Laplace 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 Laplace 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.

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_diag(), tfd_vector_exponential_linear_operator(), tfd_vector_laplace_diag(), 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()