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- W2905266186 abstract "We propose the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for model selection in hidden Markov models (HMM) when the number of states is unknown. The exact solutions exploit the properties of HMM that allow tractable forms of both criteria to be obtained while transgressing the common assumption in AIC and BIC model selection approaches on the independence of data. The proposed algorithm is presented and evaluated in application to blind channel estimation and symbol detection when the channel length is assumed unknown." @default.
- W2905266186 created "2018-12-22" @default.
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- W2905266186 date "2019-02-01" @default.
- W2905266186 modified "2023-10-02" @default.
- W2905266186 title "Akaike and Bayesian Information Criteria for Hidden Markov Models" @default.
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- W2905266186 doi "https://doi.org/10.1109/lsp.2018.2886933" @default.
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