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Extra info for Analysis and stochastics of growth processes and interface models
Hence this part requires a separate proof. Details appear in Deuschel and Zeitouni (1999) and Sepp¨ al¨ ainen (1998d). Further remarks. 7 has explicit rate functions, this large deviation problem remains unﬁnished in an important sense. It is not understood how the system behaves to create a deviation, and it is not clear what the rate functions I and U represent. The present proofs are too indirect. 5) how a large deviation problem ideally should be understood. To create a deviation Sn ≈ nu with u > v, the entire walk behaves as a random walk with mean step u.
B. (2006). Last-passage percolation with general weight distribution. Markov Process. Related Fields 12(2), 273–99. Mehta, M. L. (2004). ), Volume 142 of Pure and Applied Mathematics (Amsterdam). Elsevier/Academic Press, Amsterdam. Pr¨ ahofer, M. and Spohn, H. (2002). Scale invariance of the PNG droplet and the Airy process. J. Statist. Phys. 108(5–6), 1071–06. Dedicated to David Ruelle and Yasha Sinai on the occasion of their 65th birthdays. Pr¨ ahofer, M. and Spohn, H. (2004). Exact scaling functions for one-dimensional stationary KPZ growth.
1 Exclusion process Probability distributions from random matrix theory were discovered as limit laws for last-passage growth models almost a decade ago. 3 (Johansson 2000). 36) where F is the Tracy–Widom GUE distribution. The distribution F ﬁrst appeared as the limit distribution of the scaled largest eigenvalue of a random Hermitian matrix from the GUE (Tracy and Widom 1994). GUE is short for Gaussian Unitary Ensemble. This means that a random Hermitian matrix is constructed by putting IID complex-valued Gaussian random variables above the diagonal, IID real-valued Gaussian random variables on the diagonal, and letting the Hermitian property determine the entries below the diagonal.