Thermodynamic Gibbs Formalism and Information Theory

Ermolaev, Victor; Verbitskiy, Evgeny

doi:10.1007/978-4-431-54907-9_28

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2016

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“…This approach is investigated in a joint work with S. Berghout, where the denoising performance of the resulting Gibbsian models is evaluated. Gibbsian algorithm performs better than DUDE: bit error rates are given in the One could also try to estimate the Gibbsian potential directly, e.g., using the estimation procedure proposed in [5]. This method showed promising performance in experiments on language classification and authorship attribution.…”

Section: Denoisingmentioning

confidence: 99%

Thermodynamics of the binary symmetric channel

Verbitskiy

2016

Pac. J. Math. Ind.

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We study a hidden Markov process which is the result of a transmission of the binary symmetric Markov source over the memoryless binary symmetric channel. This process has been studied extensively in Information Theory and is often used as a benchmark case for the so-called denoising algorithms. Exploiting the link between this process and the 1D Random Field Ising Model (RFIM), we are able to identify the Gibbs potential of the resulting Hidden Markov process. Moreover, we obtain a stronger bound on the memory decay rate. We conclude with a discussion on implications of our results for the development of denoising algorithms.

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Section: Denoisingmentioning

confidence: 99%