no code implementations • 10 Dec 2020 • Mikkel B. Lykkegaard, Grigorios Mingas, Robert Scheichl, Colin Fox, Tim J. Dodwell
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications.
Probabilistic Programming Computation
no code implementations • 1 Jul 2020 • Mikkel B. Lykkegaard, Tim J. Dodwell, David Moxey
Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management.
Computation