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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/110232


    Title: Real-Time Bayesian Parameter Estimation for Item Response Models
    Authors: 翁久幸
    Weng, Ruby Chiu-Hsing;Coad, D. Stephen
    Contributors: 統計系
    Date: 2018-03
    Issue Date: 2017-06-12 17:43:25 (UTC+8)
    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.
    Relation: BAYESIAN ANALYSIS, 13(1), 115-137
    Data Type: article
    DOI link: http://dx.doi.org/10.1214/16-BA1043
    DOI: 10.1214/16-BA1043
    Appears in Collections:[Department of Statistics] Periodical Articles

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