tfb_correlation_cholesky: Maps unconstrained reals to Cholesky-space correlation...

In tfprobability: Interface to 'TensorFlow Probability'

Maps unconstrained reals to Cholesky-space correlation matrices.

Description

This bijector is a mapping between R^{n} and the n-dimensional manifold of Cholesky-space correlation matrices embedded in R^{m^2}, where n is the (m - 1)th triangular number; i.e. n = 1 + 2 + ... + (m - 1).

Usage

tfb_correlation_cholesky(validate_args = FALSE, name = "correlation_cholesky")

Arguments

validate_args	Logical, default FALSE. Whether to validate input with asserts. If validate_args is FALSE, and the inputs are invalid, correct behavior is not guaranteed.
name	name prefixed to Ops created by this class.

Details

Mathematical Details

The image of unconstrained reals under the CorrelationCholesky bijector is the set of correlation matrices which are positive definite. A correlation matrix can be characterized as a symmetric positive semidefinite matrix with 1s on the main diagonal. However, the correlation matrix is positive definite if no component can be expressed as a linear combination of the other components. For a lower triangular matrix L to be a valid Cholesky-factor of a positive definite correlation matrix, it is necessary and sufficient that each row of L have unit Euclidean norm. To see this, observe that if L_i is the ith row of the Cholesky factor corresponding to the correlation matrix R, then the ith diagonal entry of R satisfies:

1 = R_i,i = L_i . L_i = ||L_i||^2

where '.' is the dot product of vectors and ||...|| denotes the Euclidean norm. Furthermore, observe that R_i,j lies in the interval [-1, 1]. By the Cauchy-Schwarz inequality:

|R_i,j| = |L_i . L_j| <= ||L_i|| ||L_j|| = 1

This is a consequence of the fact that R is symmetric positive definite with 1s on the main diagonal. The LKJ distribution with input_output_cholesky=TRUE generates samples from (and computes log-densities on) the set of Cholesky factors of positive definite correlation matrices. The CorrelationCholesky bijector provides a bijective mapping from unconstrained reals to the support of the LKJ distribution.

Value

a bijector instance.

References

• Stan Manual. Section 24.2. Cholesky LKJ Correlation Distribution.

• Daniel Lewandowski, Dorota Kurowicka, and Harry Joe, "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis 100 (2009), pp 1989-2001.

See Also

For usage examples see tfb_forward(), tfb_inverse(), tfb_inverse_log_det_jacobian().

Other bijectors: tfb_absolute_value(), tfb_affine_linear_operator(), tfb_affine_scalar(), tfb_affine(), tfb_ascending(), tfb_batch_normalization(), tfb_blockwise(), tfb_chain(), tfb_cholesky_outer_product(), tfb_cholesky_to_inv_cholesky(), tfb_cumsum(), tfb_discrete_cosine_transform(), tfb_expm1(), tfb_exp(), tfb_ffjord(), tfb_fill_scale_tri_l(), tfb_fill_triangular(), tfb_glow(), tfb_gompertz_cdf(), tfb_gumbel_cdf(), tfb_gumbel(), tfb_identity(), tfb_inline(), tfb_invert(), tfb_iterated_sigmoid_centered(), tfb_kumaraswamy_cdf(), tfb_kumaraswamy(), tfb_lambert_w_tail(), tfb_masked_autoregressive_default_template(), tfb_masked_autoregressive_flow(), tfb_masked_dense(), tfb_matrix_inverse_tri_l(), tfb_matvec_lu(), tfb_normal_cdf(), tfb_ordered(), tfb_pad(), tfb_permute(), tfb_power_transform(), tfb_rational_quadratic_spline(), tfb_rayleigh_cdf(), tfb_real_nvp_default_template(), tfb_real_nvp(), tfb_reciprocal(), tfb_reshape(), tfb_scale_matvec_diag(), tfb_scale_matvec_linear_operator(), tfb_scale_matvec_lu(), tfb_scale_matvec_tri_l(), tfb_scale_tri_l(), tfb_scale(), tfb_shifted_gompertz_cdf(), tfb_shift(), tfb_sigmoid(), tfb_sinh_arcsinh(), tfb_sinh(), tfb_softmax_centered(), tfb_softplus(), tfb_softsign(), tfb_split(), tfb_square(), tfb_tanh(), tfb_transform_diagonal(), tfb_transpose(), tfb_weibull_cdf(), tfb_weibull()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.