Bayesian Parameter Inference for Stochastic Models – BISTOM

Co-PIs:

  • Carlo Albert (Eawag),
  • Dr. Antonietta Mira (USI)
  • Dr. Christian Rüegg (PSI)

Problem:

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.

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.

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.