abc-sde: A MATLAB toolbox for Approximate Bayesian Computation (ABC) in stochastic differential equation models. It performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations (SDEs) and not limited to the ”state-space” modelling framework. Both one- and multi-dimensional SDE systems are supported and partially observed systems are easily accommodated. Variance components for the ”measurement error” affecting the data/observations can be estimated. A 50-pages Reference Manual is provided with two case-studies implemented and discussed. The methodology is based on the research article available at http://arxiv.org/abs/1204.5459
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
- Picchini, Umberto; Anderson, Rachele: Approximate maximum likelihood estimation using data-cloning ABC (2017)
- Picchini, Umberto; Forman, Julie Lyng: Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation (2016)