Approximate Bayesian Computation (ABC) is a data-driven approach, which uses experimental or higher fidelity data to approximate the probability distribution of model parameters. ABC is based on the Bayesian approach but does not require knowing the analytical expression for a likelihood function. The primary advantages of ABC are its lower cost relative to full Bayesian methods and its flexibility in parameter estimation for complex models, e.g., turbulence models, which consist of partial differential equations.
turbABC combines ABC with Markov chain Monte Carlo (ABC-MCMC) sampling, an adaptive proposal, and calibration
steps to accelerate the parameter estimation process. It is extremely flexible and applicable to a
large suite of problems.
Olga Doronina
Postdoctoral Researcher
Working on data-driven turbulence modeling using an Approximate Bayesian Computation (ABC) approach.
Peter Hamlington
Associate Professor
Peter is an associate professor in the Paul M. Rady Department of Mechanical Engineering at the University of Colorado Boulder and the principal investigator of the Turbulence and Energy Systems Laboratory.