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Introduction to Hidden Semi-Markov Models

Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction;

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Hidden Markov Models for Time Series

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models;

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Hidden Markov Models for Time Series

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models;

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Hidden Semi-Markov Models

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since;

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Semi-Markov Processes

semi-Markov processes and their applications in reliability and maintenance. The bookexplains how to construct semi-Markov models and discusses;

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Introduction to Stochastic Models

as physics, engineering, biology and genetics, economics and social sciences. It covers Markov and semi-Markov models, as well as their;

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Semi-Markov Models

introduction to the area, the book covers semi-Markov models for different control strategies in one-component systems, defining their stationary;

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Mixture and Hidden Markov Models with R

of mixture models, to model transitions between states over time. Covering both mixture and hidden Markov models in a single book allows main;

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Inference for Hidden Markov Models and related Models

Inference for Hidden Markov Models and related Models is een boek van Cuvillier Verlag;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes;

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Inference in Hidden Markov Models

range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states;

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Hidden Markov Models

behind hidden Markov models using MATLAB (R). This approach, by means of analysis followed by synthesis, is suitable for those who want to study;

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Hidden Markov Models

behind hidden Markov models using MATLAB (R). This approach, by means of analysis followed by synthesis, is suitable for those who want to study;

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Markov Models for Pattern Recognition

. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions;

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Markov Chains

management and modeling of financial data. This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on;

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Markov Processes and Applications

include: * The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes.* An introduction to;

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Hidden Markov Models In Finance

A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance;

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Hidden Markov Models for Time Series

>Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types;

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Semi-Dirichlet Forms and Markov Processes

monograph, we intend to generalize the theory to non-symmetric and time dependent semi-Dirichlet forms. By this generalization, we can cover the;

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Hidden Markov and Other Models for Discrete-Valued Time Series

developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time;

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Markov Chains and Decision Processes for Engineers and Managers

chains with rewards so as to maximize expected rewards. An introduction to state reduction and hidden Markov chains rounds out the coverage;

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Markov Chains and Decision Processes for Engineers and Managers

chains with rewards so as to maximize expected rewards. An introduction to state reduction and hidden Markov chains rounds out the coverage;

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Earthquake Statistical Analysis through Multi-state Modeling

means of hidden (semi-)Markov models and discuss associated characteristic measures and relative estimation aspects. Further comparisons, the;

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An Introduction to Machine Learning

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches;

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Hidden Markov Processes

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems;

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Probabilistic Graphical Models

This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective;

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Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes

This book analyzes stochastic evolutionary models under the impulse of diffusion, as well as Markov and semi-Markov switches. Models are;

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