getdocf6e2. 86KB Jun 04 2011 12:05:13 AM
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In this paper, we give estimates of ideal or minimal distances between the distribution of the normalized partial sum and the limiting Gaussian distribution for stationary
We give a new representation of fractional Brownian motion with Hurst parameter H ≤ 1 2 using stochastic partial differential equations.. This representation allows us to use the
Recently, in [MR07c], we have shown that the edge-reinforced random walk on any locally finite graph has the same distribution as a random walk in a random environment given by
We present a stochastic process with sawtooth paths whose distribution is given by a simple rule and whose stationary distribution is Gaussian.. The process arose in a natural way
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
the cycle as the left endpoint of the second edge coincides with the right endpoint of the first edge and the right endpoint of the second edge coincides with the left endpoint of
In this article, we will explore why Karlin-McGregor method of using orthogonal polynomials in the study of Markov processes was so successful for one dimensional nearest
The process of good edges will still be 30-dependent and the probability of an edge being good will be uniformly large provided the slopes of the renormalized edges lie strictly
