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Applied Probability Models with Optimization Applications

some of the stochastic processes that frequently arise in applied probability. Emphasis is on optimization models and methods, particularly;

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Optimisation in Signal and Image Processing

approach; hidden Markov models; and, metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization;

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Markov Logic

resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been;

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Markov Logic

resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been;

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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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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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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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Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

This Springer brief addresses the challenges encountered in the study of the optimization of time-nonhomogeneous Markov chains. It;

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Mean Field Simulation for Monte Carlo Integration

; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters;

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Mean Field Simulation for Monte Carlo Integration

; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters;

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Markov Chains and Decision Processes for Engineers and Managers

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and;

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Markov Chains and Decision Processes for Engineers and Managers

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and;

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Discriminative Learning for Speech Recognition

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for;

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Semi-Markov Processes

Semi-Markov Processes: Applications in System Reliability and Maintenance is a modern view of discrete state space and continuous time;

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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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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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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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Simulation-Based Optimization

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and;

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Hidden Markov Models in Finance

Since the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment;

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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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Statistical Topics and Stochastic Models for Dependent Data with Applications

corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov;

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Automated Technology for Verification and Analysis

were organized in topical sections named: keynote; Markov models, chains, and decision processes; counter systems, automata; parallelism;

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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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Markov Random Field Modeling in Image Analysis

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables;

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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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