An Asymptotically Efficient Metropolis-Hastings Sampler for Bayesian Inference in Large-Scale Educational Measuremen
Computation
2018-08-14 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1808.03947,
title = {An Asymptotically Efficient Metropolis-Hastings Sampler for Bayesian Inference in Large-Scale Educational Measuremen},
author = {Timo Bechger and Gunter Maris and Maarten Marsman},
journal= {arXiv preprint arXiv:1808.03947},
year = {2018}
}