HIGHLIGHTS
- who: Sebastian Springer and collaborators from the of Computational and Process Engineering, Lappeenranta University of Technology, Lappeenranta, Finland have published the paper: Efficient Bayesian inference for large chaotic dynamical systems, in the Journal: (JOURNAL)
- what: The authors develop an inexpensive surrogate for the log likelihood with the local approximation Markov chain Monte Carlo method which in the simulations reduces the time required for accurate inference by orders of magnitude. The authors investigate the behavior of the resulting algorithm with two smaller-scale problems and then use a quasigeostrophic model to demonstrate its large-scale application . . .
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