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Myopic Posterior Sampling for Adaptive Bayesian Design of Experiments
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examples
mps
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__init__.py
requirements.txt
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README.md

MPS

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

MPS is a general and flexible framework adaptive goal oriented design of experiments.

In adaptive design of experiments (DoE), one wishes to design a sequence of experiments and collect data so as to achieve a desired goal. While there are many algorithms for specialised settings for adaptive DoE (such as optimisation, active learning, level set estimation etc.), MPS aims to provide a general framework that encompasses a broad variety of problems, including those mentioned above. To do so, one must specifiy their goal via a reward function. For more details, see our paper.

This library is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested on Linux and macOS platforms.

 

Installation & Getting Started

This library can be installed via the following commands.

$ git clone https://github.com/kirthevasank/mps
$ cd mps
$ python setup.py install

Testing the installation: Once done, you may test the installation by importing mps in the python shell.

$ python
$ import mps

Getting started: To help get started, we have provided a few example scripts in the examples directory. Simply cd examples and run the script using python, e.g. python al_linear_rbf.py.

 

Acknowledgements

Research and development of the methods in this package were funded by the Toyota Research Institute, Accelerated Materials Design & Discovery (AMDD) program.

Citation

If you use any part of this code in your work, please cite our ICML 2019 paper.

@inproceedings{kandasamy2019myopic,
  title={Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments},
  author={Kandasamy, Kirthevasan and Neiswanger, Willie and Zhang, Reed and Krishnamurthy,
Akshay and Schneider, Jeff and Poczos, Barnabas},
  booktitle={International Conference on Machine Learning},
  pages={3222--3232},
  year={2019}
}

License

This software is released under the MIT license. For more details, please refer LICENSE.txt.

For questions, please email kandasamy@cs.cmu.edu.

"Copyright 2018-2019 Kirthevasan Kandasamy"

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