Directory UMM :Data Elmu:jurnal:S:Stochastic Processes And Their Applications:Vol89.Issue2.2000:
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We employ the interlacing construction to show that the solutions ofstochastic dierential equations on manifolds which are written in Marcus canonical form and driven by
In this paper, we study small ball probabilities for Gaussian Markov processes under the L p -norms, which can also be viewed as for Brownian motion under weighted.. L
i.e. intervals in 1-D, are closed intervals so that touching grains have an end-point in common.) To see this, observe that any point t ∈ U is not covered because the grains
We study an innite system of Brownian hard balls, moving in R d and submitted to a smooth innite range pair potential. It is represented by a diusion process, which is constructed
Applying this lemma, one can conclude that, for example, the normal distribution, Weibull distributions with the parameter p¿ 1 and Poisson distribution have a light right tail..
Note that Iosifescu and Grigorescu (1990) present a wide range of pointwise a.s., log–log laws and weak convergence results and some invariance principles for dependent
It turns out that in this one-dimensional situation the (additional) mass production at a single point is enough to guarantee that the process does not exhibit local extinction
These results seem to indicate that, while estimation of ruin probabilities may be more efficient using importance sampling, especially for larger values of u , this is not the
