Math @ Duke

Publications [#147643] of Mark Huber
Papers Published
 M. Huber, Perfect simulation with exponential tails,
Random Structures and Algorithms, vol. 33 no. 1
(August, 2008),
pp. 2943, Wiley InterScience
(last updated on 2008/07/07)
Abstract: Monte Carlo algorithms typically need to generate random variates from a probability distribution described by an unnormalized density or probability mass function. Perfect simulation algorithms generate random variates exactly from these distributions, but have a running time T that is itself an unbounded random variable. This paper shows that commonly used protocols for creating perfect simulation algorithms, such as Coupling From the Past can be used in such a fashion that the running time is unlikely to be very much larger than the expected running time.


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