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

@article{Pieschner2020BayesianIF,
  title={Bayesian inference for diffusion processes: using higher-order approximations for transition densities},
  author={Susanne Pieschner and C. Fuchs},
  journal={Royal Society Open Science},
  year={2020},
  volume={7}
}
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 approximate the transition densities of the process numerically, both for calculating the posterior densities and proposing auxiliary data. Here, the Euler–Maruyama scheme is the standard approximation… Expand

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