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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...
Gorde:
| Argitaratua izan da: | R Soc Open Sci |
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| Egile Nagusiak: | , |
| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
The Royal Society
2020
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| Gaiak: | |
| Sarrera elektronikoa: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7657901/ https://ncbi.nlm.nih.gov/pubmed/33204444 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1098/rsos.200270 |
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