140 citations · 358 across the 7 of their papers we have counts for
9 papers
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics
Alfredo Garbuno-Inigo, Tapio Helin, Franca Hoffmann +1
In recent years, Bayesian inference in large-scale inverse problems found in science, engineering and machine learning has gained significant attention. This paper examines the rob…
Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions
Tapio Schneider, Oliver R. A. Dunbar, Jinlong Wu +8
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts…
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM
Oliver R. A. Dunbar, Alfredo Garbuno-Inigo, Tapio Schneider +1
Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of unc…
History matching with probabilistic emulators and active learning
Alfredo Garbuno-Inigo, F. Alejandro DiazDelaO, Konstantin M. Zuev
The scientific understanding of real-world processes has dramatically improved over the years through computer simulations. Such simulators represent complex mathematical models th…
Calibrate, Emulate, Sample
Emmet Cleary, Alfredo Garbuno-Inigo, Shiwei Lan +2
Many parameter estimation problems arising in applications are best cast in the framework of Bayesian inversion. This allows not only for an estimate of the parameters, but also fo…
Affine invariant interacting Langevin dynamics for Bayesian inference
Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich
We propose a computational method (with acronym ALDI) for sampling from a given target distribution based on first-order (overdamped) Langevin dynamics which satisfies the property…
Interacting Langevin Diffusions: Gradient Structure And Ensemble Kalman Sampler
Alfredo Garbuno-Inigo, Franca Hoffmann, Wuchen Li +1
Solving inverse problems without the use of derivatives or adjoints of the forward model is highly desirable in many applications arising in science and engineering. In this paper,…
Bayesian updating and model class selection with Subset Simulation
F. A. DiazDelaO, A. Garbuno-Inigo, S. K. Au +1
Identifying the parameters of a model and rating competitive models based on measured data has been among the most important but challenging topics in modern science and engineerin…
Transitional annealed adaptive slice sampling for Gaussian process hyper-parameter estimation
A. Garbuno-Inigo, F. A. DiazDelaO, K. M. Zuev
Surrogate models have become ubiquitous in science and engineering for their capability of emulating expensive computer codes, necessary to model and investigate complex phenomena.…