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題名 Real-Time Bayesian Parameter Estimation for Item Response Models
作者 翁久幸
Weng, Ruby Chiu-Hsing;Coad, D. Stephen
貢獻者 統計系
日期 2018-03
上傳時間 12-Jun-2017 17:43:25 (UTC+8)
摘要 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.
關聯 BAYESIAN ANALYSIS, 13(1), 115-137
資料類型 article
DOI http://dx.doi.org/10.1214/16-BA1043
dc.contributor 統計系-
dc.creator (作者) 翁久幸zh_TW
dc.creator (作者) Weng, Ruby Chiu-Hsing;Coad, D. Stephen-
dc.date (日期) 2018-03-
dc.date.accessioned 12-Jun-2017 17:43:25 (UTC+8)-
dc.date.available 12-Jun-2017 17:43:25 (UTC+8)-
dc.date.issued (上傳時間) 12-Jun-2017 17:43:25 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110232-
dc.description.abstract (摘要) 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.-
dc.format.extent 448639 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) BAYESIAN ANALYSIS, 13(1), 115-137-
dc.title (題名) Real-Time Bayesian Parameter Estimation for Item Response Models-
dc.type (資料類型) article-
dc.identifier.doi (DOI) 10.1214/16-BA1043-
dc.doi.uri (DOI) http://dx.doi.org/10.1214/16-BA1043-