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Real-time Bayesian parameter estimation for item response models - with application to Internet ratings data

Bayesian item response models have been used in modelling Internet ratings data. The statistical analysis carried out using Markov Chain Monte Carlo methods may not be computationally feasible when real-time 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.