MUQ is a library for inverse and forward uncertainty quantification.

Our goal is to provide an easy-to-use framework for defining and solving UQ problems with complex models in c++ and Python.


Sampling with advanced MCMC techniques including adaptive, multi-level, and dimension independent methods.


Advanced tools for combining model components and computing gradients and Hessian-actions.

Gaussian Processes

Regression and prior modeling using GPs, with support for derivative observations and Karhunen-Loeve expansions.

Polynomial Chaos

Support for pseudospectral construction of PCEs, forward UQ, and global sensitivity analysis.

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This material is based upon work supported by the National Science Foundation under Grant No. 1550487.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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This material is based upon work supported by the US Department of Energy, Office of Advanced Scientific Computing Research, SciDAC (Scientific Discovery through Advanced Computing) program under awards DE-SC0007099 and DE-SC0021226, for the QUEST and FASTMath SciDAC Institutes.