Bayesian Reduced-Rank Regression with Stan

Abstract

Reduced-rank regression enables characterizing the relationship between several predictors and outcome measures when their relationship can be accounted for with a relatively small number of latent dimensions. In contrast to full-rank multivariate regression, reduced-rank regression avoids estimating redundant regression coefficients and efficiently uncovers the underlying lower-dimensional latent variables that characterize the relationship between predictors and outcomes. Here, we report on an implementation of reduced-rank regression in a Bayesian framework using Markov Chain Monte Carlo, No- U-Turn Sampling as implemented in Stan, a popular open-source Bayesian inference engine. This implementation supports robust error modelling and calculation of posterior uncertainty intervals.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2019
Accession Number
AD1076799

Entities

People

  • Benjamin T. Files
  • Mac Strelioff
  • Rasmus Bonnevie

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Inference
  • Bayesian Networks
  • Coefficients
  • Computational Science
  • Data Analysis
  • Data Science
  • Inference Engines
  • Information Science
  • Language
  • Models
  • Monte Carlo Method
  • Probability
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Uncertainty

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference