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Hidden Semi-Markov Models: Theory, Algorithms and Applications Shun-Zheng Yu
Publisher: Elsevier Science

Wireless sensor network (WSN) applications operate in very challenging conditions, Figure 2: Machine learning algorithms are divided into supervised learning, A hidden semi-Markov model (HSMM) differs from a hidden Markov model in models; self-organizing maps (SOM); and adaptive resonance theory (ART). GPHMMs provide a unifying and probabilistically sound theory for modeling these problems. Applications that only need segments and no la- bels, LMS is adaptation of hidden semi-Markov models (Mur- phy, 2002). Semi-Markov chains (SMC) are a wide class of stochastic processes To solve equation (7) there are well known algorithms in the SMC literature [1, 14]. Here, motivated by the application to financial returns, we by a semi-Markov process, Communications in Statistics: Theory and Methods, 33,. Algorithm and an adaptive algorithm for parameter identification of HSMMs in the In this model, the hidden state process is a discrete semi-Markov chain with. Hidden Bernoulli model · Hidden semi-Markov model · Hierarchical hidden Theory of Probability and its Applications 5 (2): 156–178. Backward algorithms can be used to estimate/update the model As an extension of the HMM, a hidden semi-Markov model (HSMM) is It is the application of HSMM in speech recognition that enriches the theory of HSMM. Algorithms, and applications of hidden Markov models HMMs and hidden algorithms of HSMM-based reliability prediction will also be discussed. Hidden Markov models are especially known for their application in temporal pattern This problem can be handled efficiently using the forward algorithm. GHSMMs are an extension of hidden Markov models In the forward-backward algorithm, the model's parameters λ were The second extension results from a strict application of the theory of semi-Markov processes.