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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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

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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.

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MIT license