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Spectral Approximate Inference

python & matlab codes used for experiments in "Spectral Approximate Inference" (ICML 2019)

Run experiment

Run evaluate.py with python3

python3 evaluate.py

By default, this will compute partition function errors of belief propagation, mean-field approximation, mini-bucket elimination and our spectral approximate inference for pairwise binary models on complete graph of 20 vertices among a range of edge coupling strengths, except for running semi-definite programming of our spectral approximate inference.

To run semi-definite programming, install CVX from http://cvxr.com/cvx/ and run 'compute_sdp_time.m' in matlab_code folder using MATLAB.

Folder descriptions

gm

This folder contains python classes related with general graphical models and pairwise binary models.

inference

This folder contains python codes for inference algorithms for estimating the partition function (belief propagation, mean-field approximation, mini-bucket elimination and our spectral approximate inference).

mat

This folder contains matlab datasets of pairwise binary graphical models.

matlab_code

This folder contains matlab codes for running semi-definite programming solver used for our spectral approximate inference. Before running 'compute_sdp_time.m', install CVX from http://cvxr.com/cvx/

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