getdoc527e. 204KB Jun 04 2011 12:04:23 AM
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We prove an almost sure limit theorem on the exact convergence rate of the maximum of standardized gaussian random walk increments.. On a conjecture of R´
In the second example, although the family of conditioned laws are tight in the Martin topology, they possess multiple limit points so that weak convergence fails altogether..
In Section 6 we prove Theorem 3.2 about the convergence of the renormalization branching process to a time-homogeneous limit.. In Section 7 , we prove the statements from
Using the Lyapunov function approach we prove that such measures satisfy different kind of functional inequalities such as weak Poincaré and weak Cheeger, weighted Poincaré and
We will prove that if a limit measure is not absolutely continuous with respect to the Lebesgue measure then the corresponding random walk on the self similar graph does not have
Finally, we note that the proof of the convergence (3) of Theorem 2 (which is given in Section 4) works as it is, when we start the Markov chain with a translation invariant
Comment. a) We first prove this in the special case that B is strictly semireal... Proposition 12 tells us that v R is real, provided this valuation.. is
In fact it is standard to obtain estimates on the exit time distribution of a reversible Markov process from a region if one has an estimate from below of the spectral gap of
