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Subsampling MCMC - an Introduction for the Survey Statistician
- Source :
- Sankhya A. 80:33-69
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- 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.
- Subjects :
- Statistics and Probability
Computer science
Inference
Survey sampling
Astrophysics::Cosmology and Extragalactic Astrophysics
Machine learning
computer.software_genre
01 natural sciences
010104 statistics & probability
symbols.namesake
0502 economics and business
Statistics::Methodology
0101 mathematics
050205 econometrics
business.industry
05 social sciences
Estimator
Sampling (statistics)
Markov chain Monte Carlo
Statistics::Computation
Bayesian statistics
ComputingMethodologies_PATTERNRECOGNITION
Scalability
symbols
Artificial intelligence
Statistics, Probability and Uncertainty
business
computer
Statistician
Subjects
Details
- ISSN :
- 09768378 and 0976836X
- Volume :
- 80
- Database :
- OpenAIRE
- Journal :
- Sankhya A
- Accession number :
- edsair.doi...........404d5ba438c88dc1f873f93e3fcd2682