Inference in Ising models
نویسندگان
چکیده
منابع مشابه
Inference in Ising Models
The Ising spin glass is a one-parameter exponential family model for binary data with quadratic sufficient statistic. In this paper, we show that given a single realization from this model, the maximum pseudolikelihood estimate (MPLE) of the natural parameter is √ aN -consistent at a point whenever the log-partition function has order aN in a neighborhood of that point. This gives consistency r...
متن کاملEfficient Exact Inference in Planar Ising Models
We give polynomial-time algorithms for the exact computation of lowest-energy states, worst margin violators, partition functions, and marginals in certain binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it does not impose any submodularity constraints; instead we require planarity to establish a correspondence wi...
متن کاملConnectivity inference with asynchronously updated kinetic Ising models
Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Hong-Li Zeng Name of the doctoral dissertation Connectivity inference with asynchronously updated kinetic Ising model Publisher School of Science Unit Department of Applied Physics Series Aalto University publication series DOCTORAL DISSERTATIONS 117/2014 Field of research Theoretical and Computational Physics Manuscript submi...
متن کاملStatistical Inference in Autoregressive Models with Non-negative Residuals
Normal residual is one of the usual assumptions of autoregressive models but in practice sometimes we are faced with non-negative residuals case. In this paper we consider some autoregressive models with non-negative residuals as competing models and we have derived the maximum likelihood estimators of parameters based on the modified approach and EM algorithm for the competing models. Also,...
متن کاملBayesian inference for low-rank Ising networks
Estimating the structure of Ising networks is a notoriously difficult problem. We demonstrate that using a latent variable representation of the Ising network, we can employ a full-data-information approach to uncover the network structure. Thereby, only ignoring information encoded in the prior distribution (of the latent variables). The full-data-information approach avoids having to compute ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bernoulli
سال: 2018
ISSN: 1350-7265
DOI: 10.3150/16-bej886