GitHub - naesseth/smc-pgm: Code implementing sequential Monte Carlo algorithms for probabilistic graphical models described in Naesseth, Lindsten and Schön, "Sequential Monte Carlo for Graphical Models", Advances in Neural Information Processing (NIPS) 27, 2014.

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Code implementing sequential Monte Carlo algorithms for probabilistic graphical models described in Naesseth, Lindsten and Schön, "Sequential Monte Carlo for Graphical Models", Advances in Neural Information Processing (NIPS) 27, 2014.

MIT license

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gmrf		gmrf
lda		lda
xymodel		xymodel
LICENSE		LICENSE
README		README

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# README #

## Sequential Monte Carlo for Graphical Models ##

* Implementations from the paper "Sequential Monte Carlo for Graphical Models", Naesseth, Lindsten and Schön (2014)

### Classical XY model ###

* Our method SMC for PGM is implemented in "xymodel.py" and "helpfunctions.py", example for running the code is available in the iPython Notebook file "runSMC_xymodel.ipynb". The method is profiled against AIS and ASIR, implemented in MATLAB/MEX. Example for running these algorithms are available in "run_xymodel.m".

### LDA evaluation ###

* The algorithms follow the implementation structure and uses the data found in Wallach et. al., "Evaluation methods for topic models", ICML 2009.

### Gaussian MRF ###

* All algorithms are run from terminal and tuning/experiment parameters are set in the files, python [FILENAME]

### Contact ###
* christian.a.naesseth@liu.se