An Asymptotically Efficient Metropolis-Hastings Sampler for Bayesian Inference in Large-Scale Educational Measuremen

التفاصيل البيبلوغرافية
العنوان: An Asymptotically Efficient Metropolis-Hastings Sampler for Bayesian Inference in Large-Scale Educational Measuremen
المؤلفون: Bechger, Timo, Maris, Gunter, Marsman, Maarten
سنة النشر: 2018
المجموعة: Statistics
مصطلحات موضوعية: Statistics - Computation
الوصف: This paper discusses a Metropolis-Hastings algorithm developed by \citeA{MarsmanIsing}. The algorithm is derived from first principles, and it is proven that the algorithm becomes more efficient with more data and meets the growing demands of large scale educational measurement.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/1808.03947
رقم الأكسشن: edsarx.1808.03947
قاعدة البيانات: arXiv