1986
DOI: 10.21236/ada170010
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Upper Bounds for Symmetric Markov Transition Functions.

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Cited by 272 publications
(346 citation statements)
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“…In particular, during the past 30 years many authors attacked the problem of describing the global behavior of the heat diffusion kernel p (t, x, y) on various Euclidean domains and manifolds. See for instance [3,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,26,30,31,33,32,34,48,52,56,55,60,61,62,63,64].…”
Section: Motivationmentioning
confidence: 99%
“…In particular, during the past 30 years many authors attacked the problem of describing the global behavior of the heat diffusion kernel p (t, x, y) on various Euclidean domains and manifolds. See for instance [3,7,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,26,30,31,33,32,34,48,52,56,55,60,61,62,63,64].…”
Section: Motivationmentioning
confidence: 99%
“…2). In two-dimensions, the walk becomes the weighted nearest neighbor walk on C(n, 2) with loops added and weighted.…”
Section: The Metropolis Algorithm In a Boxmentioning
confidence: 99%
“…[21], [9], [12], [4], [18], [5]). Since the work of Nash [19] and Aronson [1], many methods have been discovered for deriving Gaussian upper and lower bounds of H(x, y, t), see e.g., [7], [18], [10], [13], [11], [17].…”
Section: Introductionmentioning
confidence: 99%