Illustration of the training algorithm for the sPHMM.

<p><b>A:</b> Example of the temporal evolution of the self-transition probabilities (colored lines) for an HMM with five states (each state in a different color). The reset procedure was applied at the beginning of each block of iterations (white and grey bands). The values of the...

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Bibliographic Details
Main Author:	Tianshu Li (1527088) (author)
Other Authors:	Giancarlo La Camera (94285) (author)
Published:	2025
Subjects:	Biotechnology Cancer Inorganic Chemistry Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified surrogate datasets built rapid state switching bayesian information criterion alternative strategy based sphmm also captures uncovered hidden states hidden markov models potentially related problem cortical data requires hmm &# 8217 enforce large self hidden states sphmm ). since models large prior &# 8216 xlink "> used together transition probabilities statistical properties second problem neural dynamics neural data model selection many reports low self higher likelihood ground truth first problem cortex tend cognitive processes careful handling
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Illustration of the training algorithm for the sPHMM.

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