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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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Stochastic Models In The Life Sciences And Their Methods Of Analysis

material in this area for interested students and researchers, notwithstanding the addition of some recent texts on stochastic modelling in the;

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Stochastic Biomathematical Models

systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and;

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Stochastic Modeling in Physical and Biological Sciences

application oriented chapter on how stochastic modeling throws light on physical sciences. Basics on branching processes and their applications are;

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Time Series Analysis

models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. Emphasizing the time domain;

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Modelling Mathematical Methods and Scientific Computation

in applied sciences and related mathematical and computational methods. After providing the general framework needed for mathematical modelling;

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Introduction to Stochastic Models

This book provides a pedagogical examination of the way in which stochastic models are encountered in applied sciences and techniques such;

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Recent Advances In Stochastic Modeling And Data Analysis

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover;

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Demography and Health Issues

applies data analysis, statistics and stochastic modeling techniques focusing on demography, population aging, mortality and health sciences. The;

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Structured Stochastic Matrices of M/G/1 Type and Their Applications

inherent in the classical analysis of a variety of stochastic models by methods of complex analysis.;

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Change-Point Analysis in Nonstationary Stochastic Models

in change-point analysis and stochastic modelling, the book includes theoretical details combined with computer simulations and practical;

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Change-Point Analysis in Nonstationary Stochastic Models

in change-point analysis and stochastic modelling, the book includes theoretical details combined with computer simulations and practical;

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Stochastic Modelling of Reactionâ Diffusion Processes

reactions, introducing stochastic simulation algorithms and mathematical methods for analysis of stochastic models. Different stochastic spatio;

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Stochastic Modelling of Reaction-Diffusion Processes

reactions, introducing stochastic simulation algorithms and mathematical methods for analysis of stochastic models. Different stochastic spatio;

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Statistical Models and Methods for Reliability and Survival Analysis

they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic;

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Handbook of Stochastic Models and Analysis of Manufacturing System Operations

the development of stochastic models and their analysis in manufacturing system operations. Given the handbook nature of the volume;

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Stochastic Models Of Tumor Latency And Their Biostatistical Applications

are systematically presented for the first time in this book. It offers new approaches to the stochastic description of tumor latency, employs;

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Stochastic Model Checking

and communication protocols which form the structure of the Internet. Stochastic model checking is an important field in stochastic analysis;

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Recent Development in Stochastic Dynamics and Stochastic Analysis

essentially timely and interesting subject and offers reviews on the recent and new developments in stochastic dynamics and stochastic analysis, and;

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Handbook of Probabilistic Models

of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach;

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Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology

This book focuses on the modeling and mathematical analysis of stochastic dynamical systems along with their simulations. The collected;

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Bayesian Analysis of Stochastic Process Models

treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling;

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Mean Field Simulation for Monte Carlo Integration

applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally;

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Mean Field Simulation for Monte Carlo Integration

applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally;

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Stochastic Models and Option Values

several interconnections in their research. The emphasis was on common methods, although the applications were also interrelated. The main topic;

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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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Reliability and Survival Analysis

sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually;

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