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Recent Advances in Reliability Theory

in the field of statistical and probabilistic methods in reliability.;

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Statistical Reliability Engineering

Proven statistical reliability analysis methods-available for the first time to engineers in the West While probabilistic methods of system;

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Probabilistic Safety Assessment for Optimum Nuclear Power Plant Life Management (PLiM)

of the reliability and ageing of components is critical for forecasting safety and directing preventative maintenance, and Probabilistic safety;

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Probabilistic Reliability Models

Practical Approaches to Reliability Theory in Cutting-Edge Applications Probabilistic Reliability Models helps readers understand and;

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Statistical Models and Methods for Reliability and Survival Analysis

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as;

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Handbook of Reliability Engineering

results originated by prominent Russian reliability specialists. Completely covers probabilistic reliability, statistical reliability and;

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Optimal Resource Allocation

Applications and Theory features the application of probabilistic and statistical methods used in reliability engineering during the different phases;

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Probabilistic Reliability Analysis of Power Systems: A Student's Introduction

methods used in reliability modelling of power system components, small systems and large systems. It also presents the benefits of probabilistic;

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Probabilistic Reliability Analysis of Power Systems

methods used in reliability modelling of power system components, small systems and large systems. It also presents the benefits of probabilistic;

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Statistical and Probabilistic Models in Reliability

divided into three sections: statistical methods, probabilistic methods, and special techniques and appli- cations. Of course, as with any;

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Statistical and Probabilistic Models in Reliability

divided into three sections: statistical methods, probabilistic methods, and special techniques and appli- cations. Of course, as with any;

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Statistical Reliability Engineering

systems and related applications in statistical reliability engineering. It is a textbook based mainly on the author's recent research and;

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Statistical Methods for the Reliability of Repairable Systems

that are nonrepairable and therefore discarded upon failure. Statistical Methods for the Reliability of Repairable Systems fills the gap in the;

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Probabilistic Reliability Engineering

solving problems of system design. Broad and authoritative in its content, Probabilistic Reliability Engineering covers all mathematical models;

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Reliability of High-Power Mechatronic Systems 2

reliability. The reliability of these critical elements is further validated digitally through new multi-physical and probabilistic models that could;

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Bayesian Inverse Problems

Gain a fundamental understanding of the use of probabilistic methods and reliability analysis techniques in engineering applications;

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Understanding Probability Models

reliability of systems Models for determining the probability of non responses in inquiries and to evaluate the missing data. The modeling of various;

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Handbook of Probabilistic Models

inference, Bayesian update, kriging, copula-statistical models, and more. Explains the application of advanced probabilistic models;

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Reliability Modelling and Analysis in Discrete Time

Reliability Modelling and Analysis in Discrete Time provides an overview of the probabilistic and statistical aspects connected with;

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Statistical Models in Earth Sciences

are modeled through probabilistic and statistical methods using linear process;

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Mathematical Models for Systems Reliability

distributions and presents formal rules for computing the reliability of nonrepairable systems that possess commonly occurring structures. The next two;

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Random Signals

filtering; and statistical estimation - the measurement and analysis of random data to determine the structure and parameter values of probabilistic;

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Inferential Models

applications, and new directions for research. It discusses alternative, meaningful probabilistic interpretations of some common inferential summaries;

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Inferential Models

, and new directions for research. It discusses alternative, meaningful probabilistic interpretations of some common inferential summaries, such;

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Statistical Inference in Stochastic Processes

Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and;

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Dynamic System Reliability

Offers timely and comprehensive coverage of dynamic system reliability theory This book focuses on hot issues of dynamic system;

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Bayesian Analysis with Python

analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although;

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