Cargando…
Bayesian inference for diffusion processes: using higher-order approximations for transition densities
Modelling random dynamical systems in continuous time, diffusion processes are a powerful tool in many areas of science. Model parameters can be estimated from time-discretely observed processes using Markov chain Monte Carlo (MCMC) methods that introduce auxiliary data. These methods typically appr...
| Autores principales: | , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Royal Society
2020
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657901/ https://www.ncbi.nlm.nih.gov/pubmed/33204444 http://dx.doi.org/10.1098/rsos.200270 |