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Found 8 papers, 2 papers with code
Rao-Blackwellization in the MCMC era
Rao-Blackwellization is a notion often occurring in the MCMC literature, with possibly different meanings and connections with the original Rao--Blackwell theorem (Rao, 1945 and Blackwell, 1947), including a reduction of the variance of the resulting Monte Carlo approximations.
Computation Statistics Theory Statistics Theory
Parallelising MCMC via Random Forests
For Bayesian computation in big data contexts, the divide-and-conquer MCMC concept splits the whole data set into batches, runs MCMC algorithms separately over each batch to produce samples of parameters, and combines them to produce an approximation of the target distribution.
Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale
Hamiltonian Monte Carlo samplers have become standard algorithms for MCMC implementations, as opposed to more basic versions, but they still require some amount of tuning and calibration.
Computation Data Structures and Algorithms
Generalized Bouncy Particle Sampler
As a special example of piecewise deterministic Markov process, bouncy particle sampler is a rejection-free, irreversible Markov chain Monte Carlo algorithm and can draw samples from target distribution efficiently.
Computation
ABC random forests for Bayesian parameter inference
We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level.
Likelihood-free Model Choice
This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017).
Reliable ABC model choice via random forests
We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with random forests and postponing the approximation of the posterior probability of the predicted MAP for a second stage also relying on random forests.
Adaptive approximate Bayesian computation
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version.
Computation
