getdoc13e9. 202KB Jun 04 2011 12:04:07 AM
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We show that the average occupation or empirical distribution vector up to time n, when variously 0 < ζ < 1, ζ > 1 or ζ = 1, converges in probability to a unique
In section 4 we use this coupling to show the uniqueness of the stationary interface, and then finish the proof of theorem 12. Stochastic compactness for the width of
In particular, when the Markov process in question is a diffusion, we obtain the integral test corresponding to a law of the iterated logarithm due to
Corollary 4.4 of the present paper (specialised to the case J = 2) establishes strong uniqueness and existence of associated reflected diffusions (with drift and diffusion
(This is also an instrumental a priori estimate in [23].) Similarly, the Brownian intersection exponent ξ (2 , 1) = 2 can be easily determined [9], and a direct proof also works
[1] study discretization schemes for stochastic differential equations with multivalued drift coefficients; Martinez and Talay [4] study discretization schemes for diffusion
But by the central limit theorem for m-dependent stationary sequence (see for example Brockwell and Davis, 1990, page 213), the latter is asymptotically normal with mean zero
We consider systems of diffusion equations that have considerable interest in Soil Science and Mathematical Biology and focus upon the problem of finding those forms of this class
