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• W2808301838 abstract "Estimation of hidden variables is among the most challenging tasks in statistical signal processing. In this context, hidden Markov chains have been extensively used due to their ability to recover hidden variables from observed ones even for large data. Such models fail, however, to handle nonstationary data when parameters are unknown. The aim of this paper is to show how the recent triplet Markov chains, strictly more general models exhibiting comparable computational cost, can be used to overcome this shortcoming in two different ways: (i) in a firmly Bayesian context by considering an additional Markov process to model the switches of the hidden variables; and, (ii) by introducing Dempster-Shafer theory to model the lack of precision of prior distributions. Moreover, we analyze both approaches and assess their performance through experiments conducted on sampled data and noised images." @default.
• W2808301838 created "2018-06-21" @default.
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• W2808301838 date "2018-01-01" @default.
• W2808301838 modified "2023-09-26" @default.
• W2808301838 title "Triplet Markov Chains Based- Estimation of Nonstationary Latent Variables Hidden with Independent Noise" @default.
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• W2808301838 doi "https://doi.org/10.1007/978-3-319-93375-7_7" @default.
• W2808301838 hasPublicationYear "2018" @default.
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