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stat.ME2026
Mixture of Directed Graphical Models for Discrete Spatial Random Fields
J. Brandon Carter, Catherine A. Calder
The paper introduces a mixture of directed graphical models (MDGMs) as a computationally efficient Bayesian alternative to traditional Markov random field approaches for modeling d…
stat.ME2026
Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
Yiwei Gong, Anna L. Smith, Dena Asta +1
Many real-world networks exhibit hierarchical, tree-like structure and heavy-tailed degree distributions, phenomena not readily captured by standard statistical models for network…
stat.ME2026
Markov Random Fields: Structural Properties, Phase Transition, and Response Function Analysis
J. Brandon Carter, Catherine A. Calder
This paper presents a focused review of Markov random fields (MRFs)--commonly used probabilistic representations of spatial dependence in discrete spatial domains--for categorical…