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ICB-DCM / pyABC

distributed, likelihood-free inference
https://pyabc.rtfd.io
BSD 3-Clause "New" or "Revised" License
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Multi-level sampling #172

Open yannikschaelte opened 5 years ago

yannikschaelte commented 5 years ago

Allow to use surrogate models in non-final iterations, and only the full, expensive, model in the last iteration. This will make things more efficient. What to do? One way that would suffice would be to make the model simulation time-aware (pass t to .sample). There might also be some more handy alternatives.

ljschumacher commented 4 years ago

This reference may be of interest: "Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling" https://arxiv.org/abs/2001.06256

yannikschaelte commented 4 years ago

Hi @ljschumacher . Thanks, this is exactly one of the ways we had in mind :smile:. Are you interested in using such a method?

ljschumacher commented 4 years ago

Yes, I would be. We have a use case where a faster deterministic model may help speed up inference for the full stochastic one

On 11 Aug 2020, at 12:43, Yannik Schälte notifications@github.com wrote:

Hi @ljschumacher https://github.com/ljschumacher . Thanks, this is exactly one of the ways we had in mind 😄. Are you interested in using such a method?

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