Adversarially Training MCMC with Non-Volume-Preserving Flows

Recently, flow models parameterized by neural networks have been used to design efficient Markov chain Monte Carlo (MCMC) transition kernels. However, inefficient utilization of gradient information of the target distribution or the use of volume-preserving flows limits their performance in sampling...

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Detalles Bibliográficos
Autores principales: Liu, Shaofan, Sun, Shiliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947447/
https://www.ncbi.nlm.nih.gov/pubmed/35327925
http://dx.doi.org/10.3390/e24030415

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947447/
https://www.ncbi.nlm.nih.gov/pubmed/35327925
http://dx.doi.org/10.3390/e24030415

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