Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and Pseudoposteriors
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866914775401758720 |
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| author | Freguglia, Victor Garcia, Nancy Lopes |
| author_facet | Freguglia, Victor Garcia, Nancy Lopes |
| contents | We consider the problem of estimating the interacting neighborhood of a Markov Random Field model with finite support and homogeneous pairwise interactions based on relative positions of a two-dimensional lattice. Using a Bayesian framework, we propose a Reversible Jump Monte Carlo Markov Chain algorithm that jumps across subsets of a maximal range neighborhood, allowing us to perform model selection based on a marginal pseudoposterior distribution of models. To show the strength of our proposed methodology we perform a simulation study and apply it to a real dataset from a discrete texture image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_05933 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and Pseudoposteriors Freguglia, Victor Garcia, Nancy Lopes Computation Machine Learning We consider the problem of estimating the interacting neighborhood of a Markov Random Field model with finite support and homogeneous pairwise interactions based on relative positions of a two-dimensional lattice. Using a Bayesian framework, we propose a Reversible Jump Monte Carlo Markov Chain algorithm that jumps across subsets of a maximal range neighborhood, allowing us to perform model selection based on a marginal pseudoposterior distribution of models. To show the strength of our proposed methodology we perform a simulation study and apply it to a real dataset from a discrete texture image analysis. |
| title | Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and Pseudoposteriors |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2204.05933 |