Includes bibliographical references.

Online resource; title from PDF title page (EBSCO, viewed October 29, 2015).

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

Machine generated contents note: ch. 1 Introduction -- 1.1. Markov Renewal Process and Semi-Markov Process -- 1.2. Hidden Markov Models -- 1.3. Dynamic Bayesian Networks -- 1.4. Conditional Random Fields -- 1.5. Hidden Semi-Markov Models -- 1.6. History of Hidden Semi-Markov Models -- ch. 2 General Hidden Semi-Markov Model -- 2.1.A General Definition of HSMM -- 2.2. Forward -- Backward Algorithm for HSMM -- 2.3. Matrix Expression of the Forward -- Backward Algorithm -- 2.4. Forward-Only Algorithm for HSMM -- 2.5. Viterbi Algorithm for HSMM -- 2.6. Constrained-Path Algorithm for HSMM -- ch. 3 Parameter Estimation of General HSMM -- 3.1. EM Algorithm and Maximum-Likelihood Estimation -- 3.2. Re-estimation Algorithms of Model Parameters -- 3.3. Order Estimation of HSMM -- 3.4. Online Update of Model Parameters -- ch. 4 Implementation of HSMM Algorithms -- 4.1. Heuristic Scaling -- 4.2. Posterior Notation -- 4.3. Logarithmic Form -- 4.4. Practical Issues in Implementation -- ch. 5 Conventional HSMMs.

Note continued: 5.1. Explicit Duration HSMM -- 5.2. Variable Transition HSMM -- 5.3. Variable-Transition and Explicit-Duration Combined HSMM -- 5.4. Residual Time HSMM -- ch. 6 Various Duration Distributions -- 6.1. Exponential Family Distribution of Duration -- 6.2. Discrete Coxian Distribution of Duration -- 6.3. Duration Distributions for Viterbi HSMM Algorithms -- ch. 7 Various Observation Distributions -- 7.1. Typical Parametric Distributions of Observations -- 7.2.A Mixture of Distributions of Observations -- 7.3. Multispace Probability Distributions -- 7.4. Segmental Model -- 7.5. Event Sequence Model -- ch. 8 Variants of HSMMs -- 8.1. Switching HSMM -- 8.2. Adaptive Factor HSMM -- 8.3. Context-Dependent HSMM -- 8.4. Multichannel HSMM -- 8.5. Signal Model of HSMM -- 8.6. Infinite HSMM and HDP-HSMM -- 8.7. HSMM Versus HMM -- ch. 9 Applications of HSMMs -- 9.1. Speech Synthesis -- 9.2. Human Activity Recognition -- 9.3.Network Traffic Characterization and Anomaly Detection.