Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions

Mehdi Molkaraie
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:256-265, 2020.

Abstract

We prove that the marginal densities of a global probability mass function in aprimal normal factor graph and the corresponding marginal densities in the dual normal factor graph are related via local mappings. The mapping depends on the Fourier transform of the local factors of the models. Details of the mapping, including its fixed points, are derived for the Ising model, and then extended to the Potts model. By employing the mapping, we can transform simultaneously all the estimated marginal densities from one domain to the other, which is advantageous if estimating the marginals can be carried out more efficiently in the dual domain.An example of particular significance is the ferromagnetic Ising model in a positive external field, for which there is a rapidly mixing Markov chain (called the subgraphs-world process) to generate configurations in the dual normal factor graph of the model. Our numerical experiments illustrate that the proposed procedure can provide more accurate estimates of marginal densities in various settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v108-molkaraie20a, title = {Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions}, author = {Molkaraie, Mehdi}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {256--265}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/molkaraie20a/molkaraie20a.pdf}, url = {https://proceedings.mlr.press/v108/molkaraie20a.html}, abstract = {We prove that the marginal densities of a global probability mass function in aprimal normal factor graph and the corresponding marginal densities in the dual normal factor graph are related via local mappings. The mapping depends on the Fourier transform of the local factors of the models. Details of the mapping, including its fixed points, are derived for the Ising model, and then extended to the Potts model. By employing the mapping, we can transform simultaneously all the estimated marginal densities from one domain to the other, which is advantageous if estimating the marginals can be carried out more efficiently in the dual domain.An example of particular significance is the ferromagnetic Ising model in a positive external field, for which there is a rapidly mixing Markov chain (called the subgraphs-world process) to generate configurations in the dual normal factor graph of the model. Our numerical experiments illustrate that the proposed procedure can provide more accurate estimates of marginal densities in various settings. } }
Endnote
%0 Conference Paper %T Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions %A Mehdi Molkaraie %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-molkaraie20a %I PMLR %P 256--265 %U https://proceedings.mlr.press/v108/molkaraie20a.html %V 108 %X We prove that the marginal densities of a global probability mass function in aprimal normal factor graph and the corresponding marginal densities in the dual normal factor graph are related via local mappings. The mapping depends on the Fourier transform of the local factors of the models. Details of the mapping, including its fixed points, are derived for the Ising model, and then extended to the Potts model. By employing the mapping, we can transform simultaneously all the estimated marginal densities from one domain to the other, which is advantageous if estimating the marginals can be carried out more efficiently in the dual domain.An example of particular significance is the ferromagnetic Ising model in a positive external field, for which there is a rapidly mixing Markov chain (called the subgraphs-world process) to generate configurations in the dual normal factor graph of the model. Our numerical experiments illustrate that the proposed procedure can provide more accurate estimates of marginal densities in various settings.
APA
Molkaraie, M.. (2020). Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:256-265 Available from https://proceedings.mlr.press/v108/molkaraie20a.html.

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