| Persi Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture notes-Monograph Series. Institute of Mathematical Sciences, Hayward, California, 1988. |
....that because of its translational invariance, the walk has a simple description in Fourier space. The Fourier transform of the amplitude is thus easily analyzed, and transformed back to the spatial domain. It is noteworthy that this technique is standard in the analysis of classical random walks [9]. A key advantage of the SchrSdinger approach is that the Fourier integrals for the amplitudes are amenable to analysis in standard ways. There is a well developed theory of the asymptotic expansion of integrals that allows us to determine the behavior of the wave function in the limit [4, 5] ....
P. Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture Notes-Monograph Series. Institute of Mathematical Statistics, Haywaxd, California, 1988.
....that because of its translational invariance, the walk has a simple description in Fourier space. The Fourier transform of the amplitude is thus easily analyzed, and transformed back to the spatial domain. It is noteworthy that this technique is standard in the analysis of classical random walks [9]. A key advantage of the Schr odinger approach is that the Fourier integrals for the amplitudes are amenable to analysis in standard ways. There is a well developed theory of the asymptotic expansion of integrals that allows us to determine the behavior of the wave function in the limit [4, 5] ....
P. Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture Notes-Monograph Series. Institute of Mathematical Statistics, Hayward, California, 1988.
....2 X : x) lim t 1 Pr[M t = xjM0 = y] independently of y 2 X . One important issue we shall study in this paper is the rate of convergence of ergodic Markov chains, i.e. how many steps one has to run M so that the distance between and Pr[M t = Delta jM0 = y] will be very small (see, e.g. [12, 14, 23]) A standard measure of the separation between two probability distributions is the variation distance. For any two random variables X and Y defined jointly on the same space, the variation distance between L(X) and L(Y ) is defined as kL(X) Gamma L(Y )k = sup A jPr[X 2 A] Gamma Pr[Y 2 A]j ....
.... of the convergence of an ergodic Markov chain M to its stationary distribution is the mixing time of M, denoted by M ( which is defined as M ( minfT 2 N : 8t T max x2X kL(M t jM0 = x) Gamma k g : The main technical tool we shall use in the paper is the coupling technique (cf. [3, 12, 17]) Definition 3.1 Let M be a discrete time Markov chain with a finite state space X . A coupling (X t ; Y t ) t2N for M is a discrete time Markov chain on X Theta X such that each of (X t ) t2N , Y t ) t2N , considered independently, is a faithful copy of M, i.e. L(X t ) L(M t jL(M0 ) ....
P. Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture Notes - Monograph Series. Institute of Mathematical Statistics, Hayward, CA, 1988.
No context found.
P. Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture Notes --- Monograph series. Institute of Mathematical Statistics, Hayward, CA, 1988.
No context found.
Persi Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture notes-Monograph Series. Institute of Mathematical Sciences, Hayward, California, 1988.
No context found.
Persi Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture notes-Monograph Series. Institute of Mathematical Sciences, Hayward, California, 1988.
No context found.
Persi Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture notes-Monograph Series. Institute of Mathematical Sciences, Hayward, California, 1988.
No context found.
Persi Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture notes-Monograph Series. Institute of Mathematical Sciences, Hayward, California, 1988.
No context found.
Persi Diaconis. Group Representations in Probability and Statistics, volume 11 of Lecture notes-Monograph Series. Institute of Mathematical Sciences, Hayward, California, 1988.
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