The Parallel C++ Statistical Library for Bayesian Inference: QUESO
Damon McDougall, Nicholas Malaya, Robert D. Moser

TL;DR
Queso is a parallel C++ library designed for Bayesian inference, specifically targeting high-dimensional inverse problems using MCMC methods to efficiently quantify uncertainty in models and predictions.
Contribution
It introduces a specialized, parallel C++ library that leverages MPI for scalable Bayesian inverse problem solving in large-scale applications.
Findings
Supports high-dimensional Bayesian inference efficiently
Utilizes parallel computing for large-scale problems
Facilitates coupling uncertainty quantification with forward models
Abstract
The Parallel C++ Statistical Library for the Quantification of Uncertainty for Estimation, Simulation and Optimization, Queso, is a collection of statistical algorithms and programming constructs supporting research into the quantification of uncertainty of models and their predictions. Queso is primarily focused on solving statistical inverse problems using Bayes's theorem, which expresses a distribution of possible values for a set of uncertain parameters (the posterior distribution) in terms of the existing knowledge of the system (the prior) and noisy observations of a physical process, represented by a likelihood distribution. The posterior distribution is not often known analytically, and so requires computational methods. It is typical to compute probabilities and moments from the posterior distribution, but this is often a high-dimensional object and standard Reimann-type…
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Taxonomy
TopicsGaussian Processes and Bayesian Inference · Probabilistic and Robust Engineering Design · Fault Detection and Control Systems
