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Efficient and Fast Sampling from Arbitrary Probability Kernels using Sliced Gibbs Sampler
An Automated Sliced Gibbs framework is proposed for fully automated Markov chain Monte Carlo sampling from arbitrary finite dimensional probability kernels. The method targets unnormalized, non-smooth, heavy tailed, and highly multimodal densities. A Cauchy transformation based effective support est
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