Bayesian inference for diffusion processes: using higher-order approximations for transition densities

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...

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Bibliographic Details
Main Authors: Susanne Pieschner, Christiane Fuchs
Format: Article
Language:English
Published: The Royal Society 2020-10-01
Series:Royal Society Open Science
Subjects:
stochastic differential equations
markov chain monte carlo
milstein scheme
parameter estimation
bayesian data imputation
Online Access:https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200270

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https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200270

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