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Minh, David D. L.; Minh, Do Le (Paul); Nguyen, Andrew L.

Layer sampling. (English) Zbl 1339.65021

Commun. Stat., Simulation Comput. 45, No. 1, 73-100 (2016).
Summary: Layer sampling is an algorithm for generating variates from a non-normalized multidimensional distribution \(p(\cdot)\). It empirically constructs a majorizing function for \(p(\cdot)\) 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.

Cited in 1 Document

MSC:

65C60 Computational problems in statistics (MSC2010)
62D05 Sampling theory, sample surveys
65C05 Monte Carlo methods
65C40 Numerical analysis or methods applied to Markov chains

Keywords:

layer sampling; Markov chain Monte Carlo; normalizing constant; regenerative; simulation; algorithm; numerical example; algorithm; rejection sampling
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