Heuristic Techniques for Constructing Hidden Markov Models of Stochastic Processes

Author:

Gavrikov Mikhail M.1,Mezentseva Anna Y.1,Sinetsky Roman M.1

Affiliation:

1. South-Russian State Polytechnic University (NPI) named after MI Platov,Dept. of Software Engineering,Novocherkassk,Russia

Publisher

IEEE

Reference17 articles.

1. Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency cepstral coefficients and fractals;nelwamondo;International Journal of Innovative Computing Information and Control,2006

2. Fundamental Limits for Learning Hidden Markov Model Parameters

3. PyHHMM: A Python library for heterogeneous hidden Markov models;moreno-pino;Cornell University Open Access Archive,0

4. Deeptime: a Python library for machine learning dynamical models from time series data

5. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

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