You can use the software in this package to efficiently sample from
(1) correlated multivariate binary random variables (multivariate Bernoulli)
(2) correlated multivariate Poisson random variables
(3) correlated random variables with arbitrary marginal statistics.
Applications include modeling and generating of artificial neural data.
The implementation includes sampling and parameter fitting for the Dichotomized Gaussian distribution. For some parameters this provides and efficient alternative to the maximum-entropy distribution, the Ising model.
Detailed information about the contents are contained in the readme-file at http://www.kyb.mpg.de/bethge/code/efficientsampling/readme.pdf. For an instruction on how to use the code, run the demo.m script.
The methods implemented here are described in two publications:
J. H. Macke, P. Berens, et al., Generating spike-trains with specified correlation-coefficients, Neural Computation, 2008 (accepted) (http://www.kyb.tuebingen.mpg.de/publication.html?publ=5205)
Matthias Bethge and Philipp Berens, Near-Maximum Entropy Models for Binary Neural Representations of Natural Images, Advances in Neural Information Processing 2008 (http://www.kyb.tuebingen.mpg.de/publication.html?publ=4729)
Philipp Berens (2022). Sampling from multivariate correlated binary and poisson random variables (https://www.mathworks.com/matlabcentral/fileexchange/20591-sampling-from-multivariate-correlated-binary-and-poisson-random-variables), MATLAB Central File Exchange. Retrieved .
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