LDP FOR INHOMOGENEOUS U-STATISTICS

• 成果类型: Article
• 署名作者: Bhattacharya, Sohom; Deb, Nabarun; Mukherjee, Sumit
• 署名单位: State University System of Florida; University of Florida; University of Chicago; Columbia University
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/24-AAP2107
• 发表日期: 2024
• 页码: 5769-5808
• 关键词: sparse graph convergence; l-p theory; Mean-field; partition-function; phase-transitions; large deviations; LIMIT-THEOREMS; models; SEQUENCES; zeros

摘要

In this paper we derive a large deviation principle (LDP) for inhomogeneous U/V-statistics of a general order. Using this, we derive a LDP for two types of statistics: random multilinear forms, and number of monochromatic copies of a subgraph. We show that the corresponding rate functions in these cases can be expressed as a variational problem over a suitable space of functions. We use the tools developed to study Gibbs measures with the corresponding Hamiltonians, which include tensor generalizations of both Ising (with noncompact base measure) and Potts models. For these Gibbs measures, we establish scaling limits of log normalizing constants, and weak laws in terms of weak* topology, which are of possible independent interest.