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Found 5 papers, 5 papers with code
Bayesian Fusion: Scalable unification of distributed statistical analyses
By means of extensive guidance on the implementation of the approach, we demonstrate theoretically and empirically that Bayesian Fusion is robust to increasing numbers of analyses, and coherently unifying analyses which do not concur.
Methodology
The Boomerang Sampler
We demonstrate theoretically and empirically that we can also construct a control-variate subsampling boomerang sampler which is also exact, and which possesses remarkable scaling properties in the large data limit.
Scalable Importance Tempering and Bayesian Variable Selection
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling.
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration.
Computation Probability 65C60, 65C05, 62F15, 60J25
