Layer Sampling |
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| Authors: | David D L Minh Andrew L Nguyen |
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| Institution: | 1. Illinois Institute of Technology, BCHS Chemistry Division, Chicago, Ilinois, USA;2. Department of Mathematics, California State University, Fullerton, California, USA |
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| Abstract: | Layer sampling is an algorithm for generating variates from a non-normalized multidimensional distribution p( · ). It empirically constructs a majorizing function for p( · ) from a sequence of layers. The method first selects a layer based on the previous variate. Next, a sample is drawn from the selected layer, using a method such as Rejection Sampling. Layer sampling is regenerative. At regeneration times, the layers may be adapted to increase mixing of the Markov chain. Layer sampling may also be used to estimate arbitrary integrals, including normalizing constants. |
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| Keywords: | Layer sampling Markov chain Monte Carlo Normalizing constant Regenerative Simulation |
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