# anorm_cd: Array normal conditional distributions. In tensr: Covariance Inference and Decompositions for Tensor Datasets

## Description

Conditional mean and variance of a subarray.

## Usage

 `1` ```anorm_cd(Y, M, S, saidx) ```

## Arguments

 `Y` A real valued array. `M` Mean of `Y`. `S` List of mode-specific covariance matrices of `Y`. `saidx` List of indices for indexing sub-array for which the conditional mean and variance should be computed. For example, ```said_x = list(1:2, 1:2, 1:2)``` will compute the conditional means and variances for the 2 by 2 by 2 sub-array Y[1:2, 1:2, 1:2]. This is conditional on every other element in `Y`.

## Details

This function calculates the conditional mean and variance in the array normal model. Let Y be array normal and let Y_a be a subarray of Y. Then this function will calculate the conditional means and variances of Y_a, conditional on every other element in Y.

Peter Hoff.

## References

Hoff, P. D. (2011). Separable covariance arrays via the Tucker product, with applications to multivariate relational data. Bayesian Analysis, 6(2), 179-196.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```p <- c(4, 4, 4) Y <- array(stats::rnorm(prod(p)), dim = p) saidx <- list(1:2, 1:2, 1:2) true_cov <- tensr::start_ident(p) true_mean <- array(0, dim = p) cond_params <- anorm_cd(Y = Y, M = true_mean, S = true_cov, saidx = saidx) ## Since data are independent standard normals, conditional mean is 0 and ## conditional covariance matrices are identities. cond_params\$Mab cond_params\$Sab ```

tensr documentation built on May 2, 2019, 2:32 p.m.