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 ...
In this way of learning SSEs with SG-MCMC and pruning, we not only achieve high prediction accuracy since SG-MCMC enhances exploration of the model-parameter space, but also reduce memory and computation cost significantly in both training and testing of NN ensembles. This is thoroughly evaluated...
Simulations for sgmcmc package Simulations for the package sgmcmc available for R. The simulations are for the companion paper available from arXiv (see Section 5). Running the simulations To run the simulations simply run the script runSimulations.R. The plot output corresponding to each simulati...
问SVGP与SGPMC实现的区别EN每个旷工都有出块(即挖矿)的权力，只要出块成功，就有系统给出的奖励，...
git clone https://github.com/jeremiecoullon/SGMCMCJax.git cd SGMCMCJax python -m pip install -e . Then run the tests withpip install -r requirements-dev.txt; make To run code style checks:make lint Citing SGMCMCJax Please use the following bibtex reference to cite this repository: ...
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MCMCpack 1.7-1 mda 0.5-4 memoise 2.0.1 merTools 0.6.2 meta 7.0-0 metadat 1.2-0 metafor 4.6-0 methods 4.3.3 mgcv 1.9-1 mgsub 1.7.3 mi 1.1 mice 3.16.0 microbenchmark 1.5.0 mime 0.12 miniCRAN 0.3.0 miniUI 0.1.1.1 minpack.lm 1.2-4 minqa 1.2.8 mirt 1.41 misc3d 0.9-...
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