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Accelerating MCMC algorithms

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Fig 1: Comparisons between random-walk Metropolis-Hastings, Gibbs sampling, and NUTS algorithm of samples corresponding to a highly correlated  250-dimensional multivariate Gaussian target
Fig 2: Elapsed time when drawing 10,000 MCMC samples with different amounts of data under the single machine and consensus Monte Carlo algorithms for a hierarchical Poisson regression
Fig 3: Percentage of numbers of data points used in each iteration of the confi- confi-dence sampler with a single 2nd order Taylor approximation at θ MAP
Fig 4: Un-normalised tempered target densities of a bimodal Gaussian mixture using inverse temperature levels β = {1, 0.1, 0.05, 0.005} respectively
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