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A typical approach to estimate an unknown quantity is to design an experiment that produces a random variable Z distributed in 0; 1] with EZ] = , run this experiment independently a number of times and use the average of the outcomes as the estimate. In this paper, we consider the case when no a priori information about Z is known except that is distributed(More)
Realistic statistical models often give rise to probability distributions that are then one should first attempt to use classical Monte Carlo methods that are based on iid Journal of the Royal Statistical Society, Series B 54, 657–699. MRI data, in Handbook of Markov Chain Monte Carlo, S. Brooks, A. Gelman. and output, VBA development environment and(More)
We consider a coalescing particle model where particles move in discrete time. At each time period, each remaining ball is independently put in one of n bins according to a probability distribution p ϭ (p 1 ,. .. , p n), and all balls put into the same bin merge into a single ball. Starting with k balls, we are interested in the properties of E[N(p, k)],(More)