Structure selection for stochastic processes in high dimensions
Markov random fields with temporal evolution and applications to neuroscience
| Full text | |
| Author(s): |
Leonardi, Florencia
;
Severino, Magno T. F.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | Stochastic Processes and their Applications; v. 180, p. 12-pg., 2025-02-01. |
| Abstract | |
We propose a global model selection criterion to estimate the graph of conditional dependencies of a random vector. By global criterion, we mean optimizing a function over the set of possible graphs, eliminating the need to estimate individual neighborhoods and subsequently combine them to estimate the graph. We prove the almost sure convergence of the graph estimator. This convergence holds, provided the data is a realization of a multivariate stochastic process that satisfies a polynomial mixing condition. These are the first results to show the consistency of a model selection criterion for Markov random fields on graphs under non-independent data. (AU) | |
| FAPESP's process: | 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat |
| Grantee: | Oswaldo Baffa Filho |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 23/13453-5 - Stochastic systems modeling |
| Grantee: | Luiz Renato Gonçalves Fontes |
| Support Opportunities: | Research Projects - Thematic Grants |