Markov Determinantal Point Process for Dynamic Random Sets
该文在行列式点过程框架下引入随机集合的马尔可夫过程，证明相邻项联合分布属于该族时转移分布与平稳分布也属于该族，并探讨参数化关系、模型约束、探索性分析及在《国家地理》主题数据上的估计。
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two neighboring terms follows the LDPP, both the transition distribution and the stationary distribution belong to the LDPP family as well. We explore how their parameterizations are related. We investigate various constrained model specifications, develop procedures for exploratory analysis of a series of sets, and discuss model estimation on both simulated data and topics published in National Geographic.