High Dimensional Inference with Random Maximum A-Posteriori Perturbations
This paper presents a new approach, called perturb-max, for high-dimensional statistical inference in graphical models that is based on applying random perturbations followed by optimization. This framework injects randomness into maximum a-posteriori (MAP) predictors by randomly perturbing the pote...
| Principais autores: | , |
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| Outros Autores: | |
| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Acesso em linha: | https://hdl.handle.net/1721.1/129369 |