VB_GMM_Expectation

Variational Bayes Expectation step

Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms. Variational methods are used to enable Bayesian analysis of very large Electronic Health Records data. For VB GMM details see Bishop (2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and Jordan (2000) <doi:10.1023/A:1008932416310>. Please see preprint of JSS-submitted paper <doi:10.48550/arXiv.2512.14272>.

Brian Buckley

VBphenoR

Variational Bayes for Latent Patient Phenotypes in EHR

Adrian O'Hagan

Marie Galligan

VB_GMM_Expectation function

<dl><dt>X</dt>
<dd>n x p data matrix (or data frame that will be converted to a matrix).</dd>
<dt>n</dt>
<dd>number of rows in X.</dd>
<dt>q_post</dt>
<dd>A list of the fitted posterior Q family at iteration t-1.</dd></dl>

Arguments

Variational Bayes Expectation step — VB_GMM_Expectation

<dl>

<dt>X</dt>
<dd>n x p data matrix (or data frame that will be converted to a matrix).</dd>


<dt>n</dt>
<dd>number of rows in X.</dd>


<dt>q_post</dt>
<dd>A list of the fitted posterior Q family at iteration t-1.</dd>

</dl>

VB_GMM_Expectation: Variational Bayes Expectation step

Description

Usage

Value

Arguments