Compute Bayesian Information Criterion (BIC) given a parameter tensor, an observed tensor, the dimension, and the rank based on cumulative logistic model. This BIC function is designed for selecting rank in the <code>fit_ordinal</code> function.

Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.

Chanwoo Lee

TensorComplete

Tensor Noise Reduction and Completion Methods

bic function

<dl><dt>ttnsr</dt>
<dd>An observed tensor.</dd>
<dt>theta</dt>
<dd>A continuous-valued tensor (latent parameters).</dd>
<dt>omega</dt>
<dd>The cut-off points.</dd>
<dt>d</dt>
<dd>Dimension of the tensor.</dd>
<dt>r</dt>
<dd>Rank of the tensor.</dd></dl>

Arguments

Bayesian Information Criterion (BIC) value. — bic

<dl>

<dt>ttnsr</dt>
<dd>An observed tensor.</dd>


<dt>theta</dt>
<dd>A continuous-valued tensor (latent parameters).</dd>


<dt>omega</dt>
<dd>The cut-off points.</dd>


<dt>d</dt>
<dd>Dimension of the tensor.</dd>


<dt>r</dt>
<dd>Rank of the tensor.</dd>

</dl>

bic: Bayesian Information Criterion (BIC) value.

Description

Usage

Value

Arguments