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Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite …

Conformal methods create prediction bands that control average coverage assuming solely i.i.d. data. Besides average coverage, one …

Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and high power in a so-called likelihood-free inference (LFI) setting; that is, a setting where the likelihood is not explicitly known but one can forward-simulate observable data according to a stochastic model. We present ACORE, a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest. We also present a goodness-of-fit procedure for checking whether the constructed tests and confidence regions are valid.

Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a …

We show how a nonparametric conditional density estimation (CDE) framework helps address three nontrivial challenges in ABC. (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to tune and compare the performance of ABC and related methods in estimating the posterior itself, rather than just certain properties of the density, and (iii) how to efficiently choose among a large set of summary statistics based on a CDE surrogate loss.

Most machine learning tools aim at creating good predictions for new samples. However, obtaining 100% is not feasible in most problems, …

Here we propose a fully nonparametric approach to conditional density estimation that reformulates CDE as a non-parametric orthogonal series problem where the expansion coefficients are estimated by regression. By taking such an approach, one can efficiently estimate conditional densities and not just expectations in high dimensions by drawing upon the success in high-dimensional regression. We show applications to photometric galaxy data, Twitter data, and line-of-sight velocities in a galaxy cluster.

Several machine learning applications use classifiers as a way of quantifying the prevalence of positive class labels in a target …

We describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two or more estimators for optimal performance. This leads to new improved photo-z estimators. We illustrate our methods on data from the Sloan Data Sky Survey and an application to galaxy-galaxy lensing.