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Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler
In this work, we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. Such parameter estimation problems can often be solved via a Bayesian inference framework. However, in many practical pr...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Royal Society
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410207/ https://www.ncbi.nlm.nih.gov/pubmed/37564064 http://dx.doi.org/10.1098/rsos.230275 |