getdoc83a8. 174KB Jun 04 2011 12:04:36 AM
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First we note that the construction of the A -process for all time is trivial, since the A -particles by themselves merely perform independent continuous time simple random walks..
The product of subsequent partial sums of independent, identically distributed, square inte- grable, positive random variables is asymptotically lognormal.. The result extends in
We prove the CLT for a random walk in a dynamical environment where the states of the environ- ment at different sites are independent Markov chains..
There are the extreme cases of unlimited resources leading to a branching rate independent of the population at the site (classical branching random walk) and the other case of
Although bounds on the tail distribution of sums of independent random variables are generally proved by means of a good estimate on the moment generating function, in the
Key words: Multivariate and functional central limit theorems, random fields, martingale limit theorems, self-interacting Markov chains, Markov chain Monte Carlo methods,
We apply this LDP to prove that the radius of convergence of the generating function of the collision local time of two independent copies of a symmetric and strongly transient
Abstract The limit process of aggregational models—(i) sum of random coefficient AR(1) processes with independent Brownian motion (BM) inputs and (ii) sum of AR(1) processes with
