BISTOM

Bayesian Parameter Inference for Stochastic Models

Started

April 1, 2018

Status

Completed

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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

Senior Data Scientist

Firat completed his undergraduate studies in Electronics Engineering at Sabanci University. He later received his MSc. in Electrical and Electronics Engineering from EPFL. He conducted his doctoral studies on medical image segmentation in Computer Vision Lab at ETH Zurich. In between, he visited INRIA (Sophia Antipolis, France) and ABB Corporate Research Center (Baden, Switzerland). His research interests revolve around computer vision and machine learning, with a focus on the medical domain. He has been with SDSC since 2019.

Firat Ozdemir

Fernando Perez-Cruz

Former Deputy Executive Director & Chief Data Scientist

Fernando Perez-Cruz received a PhD. in Electrical Engineering from the Technical University of Madrid. He is Titular Professor in the Computer Science Department at ETH Zurich and Head of Machine Learning Research and AI at Spiden. He has been a member of the technical staff at Bell Labs and a Machine Learning Research Scientist at Amazon. Fernando has been a visiting professor at Princeton University under a Marie Curie Fellowship and an associate professor at University Carlos III in Madrid. He held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), and BioWulf Technologies (New York). Fernando Perez-Cruz has served as Chief Data Scientist at the SDSC from 2018 to 2023, and Deputy Executive Director of the SDSC from 2022 to 2023

Fernando Perez-Cruz

PI | Partners:

Eawag, Mathematical Methods in Environmental Research:

Dr. Carlo Albert

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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

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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.

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Gallery

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Additional resources

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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 