I've uploaded a pre-print of my new manuscript! Get it here!
Blurb: In this manuscript, I describe an efficient multi-level Approximate Bayesian Computation (ABC) method for investigating model parameters. In this work, sample paths are generated with varying temporal resolutions. I start by generating low-resolution sample paths, which require only limited computational resources to construct. Those sample paths that compare well with experimental data are selected, and the temporal resolutions of the chosen sample paths are recursively increased. As such, the sample paths unlikely to aid in parameter inference are discarded at an early stage, leading to an optimal use of computational resources.
Chris Lester, Multi-level Approximate Bayesian Computation.
Chris Lester, Christian Yates, Ruth Baker, Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches. The Journal of Computational Physics, 2018. [Journal Link]
Chris Lester, Ruth Baker, Christian Yates, Parameter sensitivity analysis for reaction-diffusion models. The Journal of Chemical Physics, 2017. [Journal Link]
Chris Lester, Ruth Baker, Mike Giles, Christian Yates, Extending the multi-level method for the simulation of stochastic biological systems, 2016. The Bulletin of Mathematical Biology, 2016. [Journal Link]
Chris Lester, Christian Yates, Mike Giles, Ruth Baker, An adaptive multi-level simulation algorithm for stochastic biological systems The Journal of Chemical Physics, 2015. [Journal Link]
Mike Giles, Chris Lester, James Whittle Non-nested adaptive timesteps in multilevel Monte Carlo computations, Monte Carlo and Quasi-Monte Carlo Methods 2014. Springer, 2015.
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