This code was downloaded from https://github.com/compops/pmh-tutorial and contains the code used to produce the results in the tutorial:
J. Dahlin and T. B. Schön, Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models. Journal of Statistical Software, Code Snippets, Volume 88, Number 2, pp. 1-41, Foundation for Open Access Statistics, 2019.
The tutorial is available as open access from Journal of Statistical Software. An R package is also provided on CRAN with the implementation of the tutorial in R. The source code (almost identical to the code in the subdirectory R/) is found at pmh-tutorial-rpkg.
r/ This is the main implementation. The complete R code developed and implemented in the tutorial. This code was used to make all the numerical illustrations in the tutorial including the figures and tables. The workspaces for these runs are also provided as a zip-file in the latest release of the code to reproduce all the figures in the tutorial.
python/ Code for Python to implement the basic algorithms covered in the tutorial. Implementations for the advanced topics are not provided. Only simple plotting is implemented and no figures or saved data from runs are provided.
matlab/ Code for MATLAB to implement the basic algorithms covered in the tutorial. Implementations for the advanced topics are not provided. Only simple plotting is implemented and no figures or saved data from runs are provided.
There is source code available for Python that implements some of the generalisations discussed in the tutorial. See the README file under python/ for more information.
See LICENSE for more information.
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