On Linear Model with Markov Signal Priors

Lan V. Truong
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:38-53, 2022.

Abstract

In this paper, we estimate free energy, average mutual information, and minimum mean square error (MMSE) of a linear model under the assumption that the source is generated by a Markov chain. Our estimates are based on the replica method in statistical physics. We show that under the MMSE estimator, the linear model with Markov sources or hidden Markov sources is decoupled into single input AWGN channels with state information available at both encoder and decoder where the state distribution follows the stationary distribution of the stochastic matrix of Markov chains. Numerical results show that the free energies and MSEs obtained via the replica method are closely approximate to their counterparts via MCMC simulations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-truong22a, title = { On Linear Model with Markov Signal Priors }, author = {Truong, Lan V.}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {38--53}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/truong22a/truong22a.pdf}, url = {https://proceedings.mlr.press/v151/truong22a.html}, abstract = { In this paper, we estimate free energy, average mutual information, and minimum mean square error (MMSE) of a linear model under the assumption that the source is generated by a Markov chain. Our estimates are based on the replica method in statistical physics. We show that under the MMSE estimator, the linear model with Markov sources or hidden Markov sources is decoupled into single input AWGN channels with state information available at both encoder and decoder where the state distribution follows the stationary distribution of the stochastic matrix of Markov chains. Numerical results show that the free energies and MSEs obtained via the replica method are closely approximate to their counterparts via MCMC simulations. } }
Endnote
%0 Conference Paper %T On Linear Model with Markov Signal Priors %A Lan V. Truong %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-truong22a %I PMLR %P 38--53 %U https://proceedings.mlr.press/v151/truong22a.html %V 151 %X In this paper, we estimate free energy, average mutual information, and minimum mean square error (MMSE) of a linear model under the assumption that the source is generated by a Markov chain. Our estimates are based on the replica method in statistical physics. We show that under the MMSE estimator, the linear model with Markov sources or hidden Markov sources is decoupled into single input AWGN channels with state information available at both encoder and decoder where the state distribution follows the stationary distribution of the stochastic matrix of Markov chains. Numerical results show that the free energies and MSEs obtained via the replica method are closely approximate to their counterparts via MCMC simulations.
APA
Truong, L.V.. (2022). On Linear Model with Markov Signal Priors . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:38-53 Available from https://proceedings.mlr.press/v151/truong22a.html.

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