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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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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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Flowgraph Models For Multistate Time-To-Event Data

processes without prior knowledge of stochastic processes----opening the door to interesting applications in survival analysis and reliability as;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Bayesian Inference for Stochastic Processes

many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete;

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Stochastic Analysis of Scaling Time Series

its own limitations. This book presents the mathematical theory behind the stochastic analysis of scaling time series, including a general;

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Theory and Statistical Applications of Stochastic Processes

Gaussian processes, Ito integration, stochastic analysis, stochastic differential equations, fractional Brownian motion and parameter estimation;

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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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Encyclopedia of Mathematics and its Applications

for the first time in book form. The authors start with a detailed analysis of Levy processes in infinite dimensions and their reproducing;

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Stationary Stochastic Processes for Scientists and Engineers

Stochastic processes are indispensable tools for development and research in signal and image processing, automatic control, oceanography;

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Dynamics of Statistical Experiments

processes, which determine the dynamics of the predictable component. The method of stochastic approximation is used for classifying statistical;

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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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Generalizations of Cyclostationary Signal Processing

correlated processes. * Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators. * Offers extensive;

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Statistical Analysis of Stochastic Processes in Time

characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying;

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Statistical Analysis of Stochastic Processes in Time

characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying;

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Applied Probability and Stochastic Processes

processes, and spectral analysis of stationary stochastic processes Comprehensive numerical discussions of examples, which replace the more;

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Applied Probability and Stochastic Processes

processes, and spectral analysis of stationary stochastic processes Comprehensive numerical discussions of examples, which replace the more;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and;

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Advanced Mathematical Tools for Automatic Control Engineers: Volume 2

time processes, including the concepts of random sequences, martingales, and limit theorems. Part III covers continuous time stochastic;

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Stochastic Calculus and Differential Equations for Physics and Finance

stochastic processes, using those methods in a unified way. The focus is on nonstationary processes, and statistical ensembles are emphasized in time;

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Recent Advances In Stochastic Modeling And Data Analysis

various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics;

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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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Stochastic Causality

analysis of probabilistic data; others address causal issues in physics, with an emphasis on physical processes that are also probabilistic-i.e;

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Topics in stochastic processes

measure theory and functional analysis. They might be used for more advanced courses on stochastic processes.;

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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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