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March 2018 Real-Time Bayesian Parameter Estimation for Item Response Models
Ruby Chiu-Hsing Weng, D. Stephen Coad
Bayesian Anal. 13(1): 115-137 (March 2018). DOI: 10.1214/16-BA1043

Abstract

Bayesian item response models have been used in modeling educational testing and Internet ratings data. Typically, the statistical analysis is carried out using Markov Chain Monte Carlo methods. However, these may not be computationally feasible when real-time data continuously arrive and online parameter estimation is needed. We develop an efficient algorithm based on a deterministic moment-matching method to adjust the parameters in real-time. The proposed online algorithm works well for two real datasets, achieving good accuracy but with considerably less computational time.

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Ruby Chiu-Hsing Weng. D. Stephen Coad. "Real-Time Bayesian Parameter Estimation for Item Response Models." Bayesian Anal. 13 (1) 115 - 137, March 2018. https://doi.org/10.1214/16-BA1043

Information

Published: March 2018
First available in Project Euclid: 19 December 2016

zbMATH: 06873720
MathSciNet: MR3737945
Digital Object Identifier: 10.1214/16-BA1043

Keywords: Bayesian inference , deterministic method , moment matching , online algorithm , Woodroofe–Stein’s identity

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Vol.13 • No. 1 • March 2018
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