text: 98,90,1021559079901093888,930764003277643777,Matias Quiroz,"Want to find out more about our work on using data subsampling to speed up MCMC algorithms? We just released a textbook style easy-to-read review on arXiv: <LINK>. @matvil @robertjk59 Just noticed that Figure 3.6 is not rendering properly. Time for some arXiv hacking! The error is now fixed and the new version, with the correct Figure 3.6, should appear on arXiv tomorrow or so.",https://arxiv.org/abs/1807.08409,"The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets. Data subsampling has recently been suggested as a way to make MCMC methods scalable on massively large data, utilizing efficient sampling schemes and estimators from the survey sampling literature. These developments tend to be unknown by many survey statisticians who traditionally work with non-Bayesian methods, and rarely use MCMC. Our article explains the idea of data subsampling in MCMC by reviewing one strand of work, Subsampling MCMC, a so called pseudo-marginal MCMC approach to speeding up MCMC through data subsampling. The review is written for a survey statistician without previous knowledge of MCMC methods since our aim is to motivate survey sampling experts to contribute to the growing Subsampling MCMC literature. ",Subsampling MCMC - An introduction for the survey statistician,3,"['Want to find out more about our work on using data subsampling to speed up MCMC algorithms? We just released a textbook style easy-to-read review on arXiv: <LINK>. @matvil @robertjk59', 'Just noticed that Figure 3.6 is not rendering properly. Time for some arXiv hacking!', 'The error is now fixed and the new version, with the correct Figure 3.6, should appear on arXiv tomorrow or so.']",18,07,380