TransportBasedInference.jl

A repository for adaptive transport maps
Author mleprovost
Popularity
3 Stars
Updated Last
12 Months Ago
Started In
June 2020

TransportBasedInference.jl

A Julia package for Bayesian inference with transport maps

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The objective of this package is to allow for easy and fast resolution of Bayesian inference problems using transport maps. The package provides tools for:

  • joint and conditional density estimation from limited samples of the target distribution using the adaptive transport map algorithm developed by Baptista et al. 1.
  • sequential inference for state-space models using one of the following algorithms: the (localized) stochastic ensemble Kalman filter (Evensen 2), the ensemble transform Kalman filter (Bishop et al. 3) and a nonlinear generalization of the stochastic ensemble Kalman filter (Spantini et al. 4).

Installation

TransportBasedInference.jl is registered in the general Julia registry. To install, type e.g.,

] add TransportBasedInference

Then, in any version, type

julia> using TransportBasedInference

Tutorials

For examples, consult the documentation or see the Jupyter notebooks in the examples folder.

References

Footnotes

  1. Baptista, R., Zahm, O., & Marzouk, Y. (2020). An adaptive transport framework for joint and conditional density estimation. arXiv preprint arXiv:2009.10303.

  2. Evensen, G., 1994. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), pp.10143-10162.

  3. Bishop, C.H., Etherton, B.J. and Majumdar, S.J., 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Monthly weather review, 129(3), pp.420-436.

  4. Spantini, A., Baptista, R., & Marzouk, Y. (2019). Coupling techniques for nonlinear ensemble filtering. arXiv preprint arXiv:1907.00389.