Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

Climate projections continue to be marred by large uncertainties, which originate in
processes that need to be parameterized, such as clouds, convection, and ecosystems. But …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

Markov chain Monte Carlo methods (MCMC) are essential tools for solving many modern-
day statistical and computational problems; however, a major limitation is the inherently …

Parameters in climate models are usually calibrated manually, exploiting only small subsets
of the available data. This precludes both optimal calibration and quantification of …

A large fraction of the smallest transiting planet candidates discovered by the Kepler and
CoRoT space missions cannot be confirmed by a dynamical measurement of the mass …

We present bajes, a parallel and lightweight framework for Bayesian inference of
multimessenger transients. bajes is a python modular package with minimal dependencies …

Background In quantitative biology, mathematical models are used to describe and analyze
biological processes. The parameters of these models are usually unknown and need to be …