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Gaussian Process model for fitting deterministic simulator output. Establish efficient and reliable likelihood optimization through hybridized DIRECT-BFGS and multi-start BFGS algorithms. Programming Language: Matlab.
approximate Bayesian computation for stochastic differential equations
...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
Author's research page is http://www.maths.lth.se/matstat/staff/umberto/
Stochastic discrete event system analysis and verification are essential in order to ensure reliability in such systems. However, models that cannot be constructed with an hand-made process need to be learned. Thus, the SDES toolbox proposes an automated solution that is embedded in Matlab to learning and analisis generalized semi-Markov processes.
This project aims to provide open source software implementing the Wedge algorithm for the estimation of parameters in dynamical systems models. This project also seeks to create tutorials and resources for those using Wedge.