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Comparing sweep strategies for stochastic relaxation


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  • Amit, Y.
  • Grenander, U.
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    The rate of convergence of various sweep strategies of stochastic relaxation for simulating multivariate Gaussian measures are calculated and compared. Each sweep strategy prescribes a method for chosing which coordinates of the random vector are to be updated. Deterministic sweep strategies in which the coordinates are updated according to a fixed order are compared to random strategies in which the coordinate to be updated is chosen through some random mechanism. In addition block updating, in which a few coordinates are updated simultaneously, is compared to single coordinate updating.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 37 (1991)
    Issue (Month): 2 (May)
    Pages: 197-222

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    Handle: RePEc:eee:jmvana:v:37:y:1991:i:2:p:197-222

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    Keywords: stochastic relaxation sweep strategies products of random affine maps rates of convergence;


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    Cited by:
    1. Shephard, N. & Pitt, M.K., 1995. "Likelihood Analysis of Non-Gaussian Parameter-Driven Models," Economics Papers 108, Economics Group, Nuffield College, University of Oxford.
    2. Levine, Richard A. & Casella, George, 2006. "Optimizing random scan Gibbs samplers," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2071-2100, November.
    3. Rosenthal, Jeffrey S., 1996. "Markov chain convergence: From finite to infinite," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 55-72, March.
    4. Tervonen, Tommi & van Valkenhoef, Gert & Baştürk, Nalan & Postmus, Douwe, 2013. "Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 224(3), pages 552-559.


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