6 papers
Confidence intervals for functionals in constrained inverse problems via data-adaptive sampling-based calibration
Michael Stanley, Pau Batlle, Pratik Patil +2
We address functional uncertainty quantification for ill-posed inverse problems where it is possible to evaluate a possibly rank-deficient forward model, the observation noise dist…
Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
Alexander Shen, Mikael Kuusela
For stochastic process models, parameter inference is often severely bottlenecked by computationally expensive likelihood functions. Simulation-based inference (SBI) bypasses this…
Downscaling land surface temperature data using edge detection and block-diagonal Gaussian process regression
Sanjit Dandapanthula, Margaret Johnson, Madeleine Pascolini-Campbell +2
Accurate and high-resolution estimation of land surface temperature (LST) is crucial in estimating evapotranspiration, a measure of plant water use and a central quantity in agricu…
Neural Conditional Simulation for Complex Spatial Processes
Julia Walchessen, Andrew Zammit-Mangion, Raphaël Huser +1
A key objective in spatial statistics is to simulate from the distribution of a spatial process at a selection of unobserved locations conditional on observations (i.e., a predicti…
Simultaneous Frequentist Calibration of Confidence Regions for Multiple Functionals in Constrained Inverse Problems
Pau Batlle, Pratik Patil, Michael Stanley +3
Many scientific analyses require simultaneous comparison of multiple functionals of an unknown signal at once, calling for multidimensional confidence regions with guaranteed simul…
Optimization-based frequentist confidence intervals for functionals in constrained inverse problems: Resolving the Burrus conjecture
Pau Batlle, Pratik Patil, Michael Stanley +2
We present an optimization-based framework to construct confidence intervals for functionals in constrained inverse problems, ensuring valid one-at-a-time frequentist coverage guar…