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:
Fernando Perez-Cruz
Firat Ozdemir

PI | Partners:

Mathematical Methods in Environmental Research:

  • Dr. Carlo Albert

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Biomedical Simulation:

  • Dr. Simone Ulzega

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Data Science Lab:

  • Prof. Dr. Antonietta Mira

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Director of 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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