Bayesian Analysis

Real-Time Bayesian Parameter Estimation for Item Response Models

Ruby Chiu-Hsing Weng and D. Stephen Coad

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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.

Article information

Source
Bayesian Anal. Volume 13, Number 1 (2018), 115-137.

Dates
First available in Project Euclid: 19 December 2016

Permanent link to this document
https://projecteuclid.org/euclid.ba/1482138050

Digital Object Identifier
doi:10.1214/16-BA1043

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

Weng, Ruby Chiu-Hsing; Coad, D. Stephen. Real-Time Bayesian Parameter Estimation for Item Response Models. Bayesian Anal. 13 (2018), no. 1, 115--137. doi:10.1214/16-BA1043. https://projecteuclid.org/euclid.ba/1482138050


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