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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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Inference in Hidden Markov Models

This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics;

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Stochastic Modeling of Scientific Data

models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing;

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Stochastic Modeling of Scientific Data

models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing;

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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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Mixture and Hidden Markov Models with R

This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models;

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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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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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Probabilistic Graphical Models

of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams;

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Hidden Markov Models

This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other;

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Hidden Markov Models

This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other;

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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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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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Statistical Inference for Piecewise-deterministic Markov Processes

Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance;

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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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Probabilistic Graphical Models

models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical;

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Probabilistic Graphical Models

models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical;

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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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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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Markov Logic

both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as;

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Markov Chains

This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end;

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Bayesian inference with INLA

required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov;

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Bayesian inference with INLA

required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov;

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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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Graphical Models for Machine Learning and Digital Communication

learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he;

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