update_beta_cpp

This function calls the C++ function `update_beta_cpp` to perturb beta matrices.

Provides methods for low-rank tensor regression with tensor-valued predictors
and scalar covariates. Model estimation is performed using stochastic optimization
with random-walk updates for low-rank factor matrices. Computationally intensive
components for coefficient estimation and prediction are implemented in C++ via
'Rcpp'. The package also includes tools for cross-validation and prediction error
assessment.

Ritwick Mondal

TensorMCMC

Tensor Regression with Stochastic Low-Rank Updates

update_beta_cpp function

<dl><dt>beta</dt>
<dd>List of p×d matrices (length = rank)</dd>
<dt>p</dt>
<dd>Number of rows in each beta matrix</dd>
<dt>d</dt>
<dd>Number of columns in each beta matrix</dd>
<dt>rank</dt>
<dd>Rank of tensor decomposition</dd>
<dt>sigma</dt>
<dd>Standard deviation of Gaussian noise</dd></dl>

Arguments

Update Beta Matrices Using C++ Random Walk — update_beta_cpp

<dl>

<dt>beta</dt>
<dd>List of p×d matrices (length = rank)</dd>


<dt>p</dt>
<dd>Number of rows in each beta matrix</dd>


<dt>d</dt>
<dd>Number of columns in each beta matrix</dd>


<dt>rank</dt>
<dd>Rank of tensor decomposition</dd>


<dt>sigma</dt>
<dd>Standard deviation of Gaussian noise</dd>

</dl>

update_beta_cpp: Update Beta Matrices Using C++ Random Walk

Description

Usage

Value

Arguments