English

Joint estimation of parameters in Ising model

Statistics Theory 2018-01-23 v1 Probability Statistics Theory

Abstract

We study joint estimation of the inverse temperature and magnetization parameters (β,B)(\beta,B) of an Ising model with a non-negative coupling matrix AnA_n of size n×nn\times n, given one sample from the Ising model. We give a general bound on the rate of consistency of the bi-variate pseudolikelihood estimator. Using this, we show that estimation at rate n1/2n^{-1/2} is always possible if AnA_n is the adjacency matrix of a bounded degree graph. If AnA_n is the scaled adjacency matrix of a graph whose average degree goes to ++\infty, the situation is a bit more delicate. In this case estimation at rate n1/2n^{-1/2} is still possible if the graph is not regular (in an asymptotic sense). Finally, we show that consistent estimation of both parameters is impossible if the graph is Erd\"os-Renyi with parameter p>0p>0 free of nn, thus confirming that estimation is harder on approximately regular graphs with large degree.

Keywords

Cite

@article{arxiv.1801.06570,
  title  = {Joint estimation of parameters in Ising model},
  author = {Promit Ghosal and Sumit Mukherjee},
  journal= {arXiv preprint arXiv:1801.06570},
  year   = {2018}
}

Comments

30 pages, 2 figures

R2 v1 2026-06-22T23:50:25.830Z