التفاصيل البيبلوغرافية
| العنوان: |
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo |
| المؤلفون: |
Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth |
| المصدر: |
Journal of Statistical Software, Vol 91, Iss 1, Pp 1-27 (2019) |
| بيانات النشر: |
Foundation for Open Access Statistics, 2019. |
| سنة النشر: |
2019 |
| المجموعة: |
LCC:Statistics |
| مصطلحات موضوعية: |
stochastic gradient markov chain monte carlo, big data, mcmc, stochastic gradient langevin dynamics, stochastic gradient hamiltonian monte carlo, stochastic gradient nos\'e-hoover thermostat, Statistics, HA1-4737 |
| الوصف: |
This paper introduces the R package sgmcmc; which can be used for Bayesian inference on problems with large data sets using stochastic gradient Markov chain Monte Carlo (SGMCMC). Traditional Markov chain Monte Carlo (MCMC) methods, such as Metropolis-Hastings, are known to run prohibitively slowly as the data set size increases. SGMCMC solves this issue by only using a subset of data at each iteration. SGMCMC requires calculating gradients of the log-likelihood and log-priors, which can be time consuming and error prone to perform by hand. The sgmcmc package calculates these gradients itself using automatic differentiation, making the implementation of these methods much easier. To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework. SGMCMC has become widely adopted in the machine learning literature, but less so in the statistics community. We believe this may be partly due to lack of software; this package aims to bridge this gap. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1548-7660 |
| Relation: |
https://www.jstatsoft.org/index.php/jss/article/view/3274; https://doaj.org/toc/1548-7660 |
| DOI: |
10.18637/jss.v091.i03 |
| URL الوصول: |
https://doaj.org/article/e1b00201787f45e1b5cc9be371bedada |
| رقم الانضمام: |
edsdoj.1b00201787f45e1b5cc9be371bedada |
| قاعدة البيانات: |
Directory of Open Access Journals |
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