R^kThe Multivariate Normal distribution is defined over R^k`` and parameterized by a (batch of) length-k loc vector (aka "mu") and a (batch of) k x kscale matrix;covariance = scale @ scale.Twhere@` denotes
matrix-multiplication.
tfd_multivariate_normal_diag_plus_low_rank(
loc = NULL,
scale_diag = NULL,
scale_identity_multiplier = NULL,
scale_perturb_factor = NULL,
scale_perturb_diag = NULL,
validate_args = FALSE,
allow_nan_stats = TRUE,
name = "MultivariateNormalDiagPlusLowRank"
)a distribution instance.
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.
Floating-point Tensor representing a rank-r
perturbation added to scale. May have shape [B1, ..., Bb, k, r],
b >= 0, and characterizes b-batches of rank-r updates to scale.
When NULL, no rank-r update is added to scale.#'
Floating-point Tensor representing a diagonal matrix
inside the rank-r perturbation added to scale. May have shape
[B1, ..., Bb, r], b >= 0, and characterizes b-batches of r x r
diagonal matrices inside the perturbation added to scale. When
NULL, an identity matrix is used inside the perturbation. Can only be
specified if scale_perturb_factor is also specified.
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.
Mathematical Details
The probability density function (pdf) is,
pdf(x; loc, scale) = exp(-0.5 ||y||**2) / Z
y = inv(scale) @ (x - loc)
Z = (2 pi)**(0.5 k) |det(scale)|
where:
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||**2 denotes the squared Euclidean norm of y.
A (non-batch) scale matrix is:
scale = diag(scale_diag + scale_identity_multiplier ones(k)) +
scale_perturb_factor @ diag(scale_perturb_diag) @ scale_perturb_factor.T
where:
scale_diag.shape = [k],
scale_identity_multiplier.shape = [],
scale_perturb_factor.shape = [k, r], typically k >> r, and,
scale_perturb_diag.shape = [r].
Additional leading dimensions (if any) will index batches.
If both scale_diag and scale_identity_multiplier are NULL, then
scale is the Identity matrix.
The MultivariateNormal distribution is a member of the
location-scale family, i.e., it can be
constructed as,
X ~ MultivariateNormal(loc=0, scale=1) # Identity scale, zero shift.
Y = scale @ X + loc
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(),
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_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()