伊辛模型的贝叶斯有限混合模型
Bayesian Finite Mixtures of Ising Models
AI总结：
该研究提出伊辛模型有限混合模型以分析二元变量关联的多变量模式，探究其可识别性条件并构建贝叶斯拟合框架，经实验验证可有效用于稀疏二元列联表分析。
摘要：
我们提出伊辛模型有限混合模型，作为研究二元变量关联多变量模式的新方法，兼具伊辛模型与多元伯努利混合模型的优势。我们探究了伊辛混合模型可识别性的必要条件，构建了用于拟合该模型的贝叶斯框架。通过仿真实验与真实数据案例验证，伊辛混合模型在稀疏二元列联表分析中可得到有意义的结果。
英文摘要：
We introduce finite mixtures of Ising models as a novel approach to study multivariate patterns of associations of binary variables. Our proposed models combine the strengths of Ising models and multivariate Bernoulli mixture models. We examine conditions required for the identifiability of Ising mixture models, and develop a Bayesian framework for fitting them. Through simulation experiments and real data examples, we show that Ising mixture models lead to meaningful results for sparse binary contingency tables.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
