paper

Diagnostics for Conditional Density Models and Bayesian Inference Algorithms

There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional features, are an integral part of uncertainty quantification in prediction …

Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones

Prevailing theory contends that tropical cyclones (TCs) are primarily driven by wind-induced heat exchange between the air and sea. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity …

Evaluation of probabilistic photometric redshift estimation approaches for LSST

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF …

Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference

It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference approaches, which are beginning to …

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist. In these cases, it is sometimes necessary to estimate an approximate …

(f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data

Random forests is a common non-parametric regression technique which performs well for mixed-type unordered data and irrelevant features, while being robust to monotonic variable transformations. Standard random forests, however, do not efficiently …

Non-Gaussianity in the Weak Lensing Correlation Function Likelihood - Implications for Cosmological Parameter Biases

We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in one-dimensional marginal distributions of shear two-point correlation …

A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting

Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the era of large-scale sky surveys is that the bright galaxies that are selected for spectroscopic observation do not …

Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias

Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly …

Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting.

In some applications (e.g., in cosmology and economics), the regression E[Z|x] is not adequate to represent the association between a predictor x and a response Z because of multi-modality and asymmetry of f(z|x); using the full density instead of a …