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Lectures on Integrable Probability: stochastic vertex models and symmetric functions

Author(s)
Borodin, Alexei; Petrov, Leonid
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Integrable probability: stochastic vertex models and symmetric functions
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
We consider a homogeneous stochastic higher spin six-vertex model in a quadrant. For this model concise integral representations for multipoint q-moments of the height function and for the q-correlation functions are derived. At least in the case of the step initial condition, these formulas degenerate in appropriate limits to many known formulas of such type for integrable probabilistic systems in the (1+1)d KPZ universality class, including the stochastic six-vertex model, ASEP, various q-TASEPs, and associated zero-range processes. The arguments are largely based on properties of a family of symmetric rational functions that can be defined as partition functions of the higher spin six-vertex model for suitable domains; they generalize classical Hall–Littlewood and Schur polynomials. A key role is played by Cauchy-like summation identities for these functions, which are obtained as a direct corollary of the Yang–Baxter equation for the higher spin six-vertex model. Keywords: six-vertex model; ASEP; TASEP; Kardar–Parisi–Zhang equation; Yang–Baxter equation; integrable probability
Date issued
2018-01
URI
https://hdl.handle.net/1721.1/122809
Department
Massachusetts Institute of Technology. Department of Mathematics
Journal
Stochastic Processes and Random Matrices: Lecture Notes of the Les Houches Summer School: Volume 104, July 2015
Publisher
Oxford University Press
Citation
Borodin, Alexei and Leonid Petrov. "Integrable probability: stochastic vertex models and symmetric functions." Stochastic Processes and Random Matrices: Lecture Notes of the Les Houches Summer School: Volume 104, July 2015. Edited by Gregory Schehr, Alexander Altland, Yan V. Fyodorov, Neil O'Connell, and Leticia F. Cugliandolo. Oxford University Press, 2017
Version: Original manuscript
ISBN
9780198797319

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