BISTOM

Bayesian Parameter Inference for Stochastic Models

Started
April 1, 2018
Status
Completed
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Abstract

For a multitude of different field sciences, determining the underlying mechanistic models is important in order to further our understanding. An accurate estimation of the parameters of such mechanistic models through data can be computationally prohibitive. This project exploits neural networks in order to learn minimal and near sufficient summary statistics as latent embeddings on simulated data for multiple stochastic models. This is corroborated with sharp model parameter posteriors observed through approximate Bayesian computation experiments.

People

Collaborators

SDSC Team:
Firat Ozdemir
Fernando Perez-Cruz

PI | Partners:

Eawag, Mathematical Methods in Environmental Research:

  • Dr. Carlo Albert

More info

ZHAW, Biomedical Simulation:

  • Dr. Simone Ulzega

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USI, Data Science Lab:

  • Prof. Dr. Antonietta Mira

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Paul Scherrer Institute:

  • Prof. Dr. Christian Rüegg

More info

description

Goal

Reliably estimating parameters of mechanistic models from data (Bayesian inference) is computationally expensive if (i) the data is big or (ii) the model is stochastic. Stochastic models are needed for reliable predictions.

Impact

The developed methodology can be applied in many fields of science and engineering, wherever system understanding (mechanistic model) needs to be combined with data, for advancing domain knowledge and making more reliable predictions.

Solution

Advances in algorithms as well as parallel computing infrastructure allow for Bayesian inference to be applied to a large class of stochastic models and to be scaled up to big data. We developed neural network based framework which can learn minimal and near sufficient summary statistics as latent embeddings on simulated data for multiple stochastic models. Experiments with approximate Bayesian computation yield sharp model parameter posteriors. For both stochastic models, developed solution finds near sufficient summary statistics.

Gallery

Annexe

Additional resources

Bibliography

Publications

Raynal, L.; Chen, S.; Mira, A.; Onnela, J. "Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries" Bayesian Analysis 17 1 2022 View publication
Albert, C.; Ulzega, S.; Ozdemir, F.; Perez-Cruz, F.; Mira, A. "Learning Summary Statistics for Bayesian Inference with Autoencoders" SciPost Physics Core 5 3 43 2022 View publication
Guratinder, K.; Schmidt, M.; Walker, H. C.; Bewley, R.; Wörle, M.; Cabra, D.; Osorio, S. A.; Villalba, M.; Madsen, A. K.; Keller, L.; et al. "Magnetic correlations in the triangular antiferromagnet FeGa2S4" Physical Review B 104 6 64412 2021 View publication
Albert, C.; Ferriz-Mas, A.; Gaia, F.; Ulzega, S. "Can Stochastic Resonance Explain Recurrence of Grand Minima?" The Astrophysical Journal Letters 916 2 L9 2021 View publication
Allenspach, S.; Puphal, P.; Link, J.; Heinmaa, I.; Pomjakushina, E.; Krellner, C.; Lass, J.; Tucker, G. S.; Niedermayer, C.; Imajo, S.; et al. "Revealing three-dimensional quantum criticality by Sr substitution in Han purple" Physical Review Research 3 2 23177 2021 View publication
Warne, D. J.; Ebert, A.; Drovandi, C.; Hu, W.; Mira, A.; Mengersen, K. "Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic" BMC Public Health 20 1 1868 2020 View publication

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