7 papers
Keeping Score: Adaptive, Tuning-Free Loss Weighting for Score-Augmented Neural Ratio Estimation
Alexander Shen, Mikael Kuusela
Neural likelihood surrogates (e.g., Neural Ratio Estimation) for stochastic process models are commonly trained via probabilistic classification on simulated data, which forces a t…
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…
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…
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…
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…
Posterior Uncertainty Estimation via a Monte Carlo Procedure Specialized for Data Assimilation
Michael Stanley, Mikael Kuusela, Brendan Byrne +1
Through the Bayesian lens of data assimilation, uncertainty on model parameters is traditionally quantified through the posterior covariance matrix. However, in modern settings inv…
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…