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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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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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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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Statistical Inference in Stochastic Processes

statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the;

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Bayesian Inference for Stochastic Processes

to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different;

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Bayesian Inference for Stochastic Processes

statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes;

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Stochastic Models, Statistics and Their Applications

analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design;

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Statistical Inference for Diffusion Type Processes

refinement of the model are the key steps or building blocks involved in any rational decision making process. Stochastic processes are widely used;

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Stochastic Modeling of Scientific Data

Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common;

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Stochastic Modeling of Scientific Data

Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common;

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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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Statistical Inference for Financial Engineering

analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear;

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Stochastic Processes - Inference Theory

fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both;

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Simulation and Inference for Stochastic Processes with Yuima: A Comprehensive R Framework for Sdes and Other Stochastic Processes

supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic;

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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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Probability in Physics

of statistical inference, as well the importance of probability in information theory, quantum mechanics, and stochastic processes, in a unified manner;

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Bayesian Analysis of Stochastic Process Models

Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified;

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Theory of Stochastic Objects

areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by;

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Theory of Stochastic Objects

areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by;

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Theory and Statistical Applications of Stochastic Processes

This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It;

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Distribution of Statistical Observables for Anomalous and Nonergodic Diffusions

modelled by non-Brownian stochastic processes in the complex real-world environment. Statistical observables are widely used for anomalous and;

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Multi-State Survival Models for Interval-Censored Data

of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide;

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Multi-State Survival Models for Interval-Censored Data

of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide;

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Probability

development of distribution theory while also introducing the reader to basic statistical inference. In this way, the major stochastic processes are;

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Partitions, Hypergeometric Systems, and Dirichlet Processes in Statistics

This book focuses on statistical inferences related to various combinatorial stochastic processes. Specifically, it discusses the;

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Hilbert Space Methods In Probability And Statistical Inference

of martingales stochastic integration, interpolation and density estimation. Includes a copious amount of problems and examples.;

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