A Student's T process (TP) is an indexed collection of random variables, any finite collection of which are jointly Multivariate Student's T. While this definition applies to finite index sets, it is typically implicit that the index set is infinite; in applications, it is often some finite dimensional real or complex vector space. In such cases, the TP may be thought of as a distribution over (real- or complex-valued) functions defined over the index set.

tfd_student_t_process(
df,
kernel,
index_points,
mean_fn = NULL,
jitter = 1e-06,
validate_args = FALSE,
allow_nan_stats = FALSE,
name = "StudentTProcess"
)

## Arguments

df Positive Floating-point Tensor representing the degrees of freedom. Must be greater than 2. PositiveSemidefiniteKernel-like instance representing the TP's covariance function. float Tensor representing finite (batch of) vector(s) of points in the index set over which the TP is defined. Shape has the form [b1, ..., bB, e, f1, ..., fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to a e-dimensional multivariate Student's T. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn. Function that acts on index_points to produce a (batch of) vector(s) of mean values at index_points. Takes a Tensor of shape [b1, ..., bB, f1, ..., fF] and returns a Tensor whose shape is broadcastable with [b1, ..., bB]. Default value: NULL implies constant zero function. float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6. 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.

## Value

a distribution instance.

## Details

Just as Student's T distributions are fully specified by their degrees of freedom, location and scale, a Student's T process can be completely specified by a degrees of freedom parameter, mean function and covariance function.

Let S denote the index set and K the space in which each indexed random variable takes its values (again, often R or C). The mean function is then a map m: S -> K, and the covariance function, or kernel, is a positive-definite function k: (S x S) -> K. The properties of functions drawn from a TP are entirely dictated (up to translation) by the form of the kernel function.

This Distribution represents the marginal joint distribution over function values at a given finite collection of points [x[1], ..., x[N]] from the index set S. By definition, this marginal distribution is just a multivariate Student's T distribution, whose mean is given by the vector [ m(x[1]), ..., m(x[N]) ] and whose covariance matrix is constructed from pairwise applications of the kernel function to the given inputs:

| k(x[1], x[1])    k(x[1], x[2])  ...  k(x[1], x[N]) |
| k(x[2], x[1])    k(x[2], x[2])  ...  k(x[2], x[N]) |
|      ...              ...                 ...      |
| k(x[N], x[1])    k(x[N], x[2])  ...  k(x[N], x[N]) |



For this to be a valid covariance matrix, it must be symmetric and positive definite; hence the requirement that k be a positive definite function (which, by definition, says that the above procedure will yield PD matrices). Note also we use a parameterization as suggested in Shat et al. (2014), which requires df to be greater than 2. This allows for the covariance for any finite dimensional marginal of the TP (a multivariate Student's T distribution) to just be the PD matrix generated by the kernel.

Mathematical Details

The probability density function (pdf) is a multivariate Student's T whose parameters are derived from the TP's properties:

pdf(x; df, index_points, mean_fn, kernel) = MultivariateStudentT(df, loc, K)
K = (df - 2) / df  * (kernel.matrix(index_points, index_points) + jitter * eye(N))
loc = (x - mean_fn(index_points))^T @ K @ (x - mean_fn(index_points))


where:

• df is the degrees of freedom parameter for the TP.

• index_points are points in the index set over which the TP is defined,

• mean_fn is a callable mapping the index set to the TP's mean values,

• kernel is PositiveSemidefiniteKernel-like and represents the covariance function of the TP,

• jitter is added to the diagonal to ensure positive definiteness up to machine precision (otherwise Cholesky-decomposition is prone to failure),

• eye(N) is an N-by-N identity matrix.

## References

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(), 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()