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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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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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Markov Renewal and Piecewise Deterministic Processes

deterministic functions of CSMPs (Completed Semi-Markov Processes), i.e. semi-Markov processes completed to become Markov processes. This remark leads;

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Piecewise Deterministic Processes in Biological Models

This book presents a concise introduction to piecewise deterministic Markov processes (PDMPs), with particular emphasis on their;

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Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes

Mark H.A. Davis introduced the Piecewise-Deterministic Markov Process (PDMP) class of stochastic hybrid models in an article in 1984. Today;

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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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Random Processes For Engineers

* Kalman and Wiener filtering * Hidden Markov models for statistical inference * The estimation maximization (EM) algorithm * An introduction to;

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Statistical Methods for Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will;

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Bayesian Inference for Stochastic Processes

in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov;

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Bayesian Inference for Stochastic Processes

in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov;

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Statistical Methods Of Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will;

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Elements of Applied Stochastic Processes

statistical inference in stochastic processes. * Integration of theory and application offers improved teachability * Provides a comprehensive;

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Semimartingales and their Statistical Inference

of semimartingales at a level needed for researchers working in the area of statistical inference for stochastic processes. The author brings together into;

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Semimartingales and Their Statistical Inference

of semimartingales at a level needed for researchers working in the area of statistical inference for stochastic processes. The author brings together into;

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Sense and Nonsense of Statistical Inference: Controversy

analysis, Shewhart control chart, descriptive statistics, chi-square test, nonlinear modeling, spectral estimation and Markov processes in quality;

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Sense and Nonsense of Statistical Inference

analysis, Shewhart control chart, descriptive statistics, chi-square test, nonlinear modeling, spectral estimation and Markov processes in quality;

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Statistical Inference for Fractional Diffusion Processes

economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications;

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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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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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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Optimal Statistical Inference in Financial Engineering

Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering;

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Markov Models & Optimization

. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class;

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Markov Logic

templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its;

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Generated Dynamics of Markov and Quantum Processes

taken as relevant variables, while Markov processes emerge when some of those variables cannot be followed and are thus irrelevant for the;

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Controlled Markov Processes and Viscosity Solutions

This book is intended as an introduction to optimal stochastic control for continuous time Markov processes and to the theory of viscosity;

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