Bayesian Optimal Nonlinear Design Code
The code provided here implements the algorithms described in our paper
SMC Samplers for Bayesian Optimal Nonlinear Design
Nonlinear Statistical Signal Processing Workshop (NSSPW), 2006
[PDF]
Download the source code: OptimalDesignSMCSamplers.zip
The code is research code and as such a little messy. It is also somewhat specialized to the specific simple example problem presented in the paper. However it should be possible to adapt it to different problems with little effort.
The algorithms are implemented in the Python programming language and make use of the following Python extension modules:
On Windows, if you do not have a working Python installation already, I recommend installing the Python Enthought edition which includes these and many other modules.
For Mac users I recommend installing the latest Active Python and the Scipy SuperPack which includes the required modules.
Please refer to the included README.txt file for further instructions.
For use on larger real world problems I recommend re-implementing the algorithm in a compiled language such as C for performance reasons.
Please feel free to contact me with any questions regarding the source code and/or the algorithms described in the paper.