Awesome

nonparametric-bayes

• Authors:
  • Carl Boettiger
  • Marc Mangel
  • Steve Munch

doi: 10.5281/zenodo.12669

This repository contains the research compendium of our work in nonparametric Bayesian inference for improving ecosystem management under deep structural uncertainty. The compendium contains all data, code, and text associated with the publication and has been permanently archived at the DOI indicated by the above badge.

R package

This repository is organized as an R package, providing functions to integrate the stochastic dynamic programming and Gaussian process inference methods explored here. Nevertheless, this package has been written explicitly for this project and may not yet be suitable for more general purpose use.

Manuscript

The results of this project have now been written up for publication. See the manuscript directory for details, including the code necessary to run the examples shown.

Notes and resources

• See my lab notebook entries under the nonparametric-bayes tag for ongoing description of this research.

• The issues tracker lists both current and accomplished goals in this project, and steps towards their completion.

• See the repository history for a fine-grained view of progress and changes.

• This is an Open Laboratory Notebook project. All work under this project has been available on the github repo as the research progresses.