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Inference in Hidden Markov Models
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states.In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models.
Computer science : Theory and applications ; 15th International computer science symposium in Russia, CSR 2020, Yekaterinburg, Russia, June 29 – July 3, 2020, Proceedings
This book constitutes the proceedings of the 15th International Computer Science Symposium in Russia, CSR 2020, held in Yekaterinburg, Russia, in June 2020. The 25 full papers and 6 invited papers were carefully reviewed and selected from 49 submissions. The papers cover a broad range of topics, such as: algorithms and data structures; computational complexity, including hardness of approximation and parameterized complexity; randomness in computing, approximation algorithms, fixed-parameter algorithms; combinatorial optimization, constraint satisfaction, operations research; computational geometry; string algorithms; formal languages and automata, including applications to computational linguistics; codes and cryptography; combinatorics in computer science; computational biology; applications of logic to computer science, proof complexity; database theory; distributed computing; fundamentals of machine learning, including learning theory, grammatical inference and neural computing; computational social choice; quantum computing and quantum cryptography; theoretical aspects of big data.
Algorithms - ESA 2008 ; 16th Annual European Symposium, Karlsruhe, Germany, September 15-17, 2008. Proceedings
This book constitutes the refereed proceedings of the 16th Annual European Symposium on Algorithms, ESA 2008, held in Karlsruhe, Germany, in September 2008 in the context of the combined conference ALGO 2008.
