Construct · 1 fields · 2 citations

sampler: a standalone construct

One name. One construct. A landmark on the map.

● anchor · a standalone construct Nearest construct by meaning·not indexed
sampler1 CONSTRUCTGibbs samplerGIBBS · DRAW SAMPLES

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Statistics

An algorithm that draws from a joint distribution

In Bayesian statistics the Gibbs sampler is a Markov-chain Monte Carlo algorithm that generates draws from a joint distribution that is hard to sample directly by cycling through its variables and sampling each in turn from its full conditional distribution given the current values of the others. Repeated sweeps produce a chain whose stationary distribution is the target joint, so the accumulated draws approximate posterior quantities that cannot be computed in closed form. Geman and Geman introduced it for image restoration, and Casella and George gave the explanation that carried it into mainstream statistics. The referent is a sampling procedure, a sequence of random draws, and it shares only the noun with a cloth or a music machine.[1][2]

Unit
Draws from the target joint distribution, one per sweep of the conditionals
Recovery means
Not applicable
Measured by
Assessed by convergence diagnostics, autocorrelation and effective sample size, and Monte Carlo standard error of the estimates

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Nearby constructs

break2 fields · 4 citationsjingle · one word, two constructschain3 fields · 3 citationsjingle · one word, three constructsclosed3 fields · 3 citationsjingle · one word, three constructs

References

  1. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6), 721–741. https://doi.org/10.1109/tpami.1984.4767596
  2. Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46(3), 167–174. https://doi.org/10.1080/00031305.1992.10475878

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