Prediction Intervals For First Order Markov Processes
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Prediction Intervals For First Order Markov Processes
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Author : Tatiraju S. Murthy
language : en
Publisher:
Release Date : 1979
Prediction Intervals For First Order Markov Processes written by Tatiraju S. Murthy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1979 with categories.
A prediction interval is an interval that contains a future observation with a pre-specified probability. The limits of the interval are functions of known observations from a family of known distributions with given parameters. Though prediction intervals resemble confidence intervals, they differ from the latter conceptually. A confidence interval covers the value of a parameter of a distribution. A prediction interval, on the other hand, encloses the value of a random variable.
A Prediction Interval For A First Order Gaussian Markov Process
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Author : Toke Jayachandran
language : en
Publisher:
Release Date : 1980
A Prediction Interval For A First Order Gaussian Markov Process written by Toke Jayachandran and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Gaussian processes categories.
Let x sub t (t = 1,2,..) be a stationary Gaussian Markov process of order one with E(x sub t) = mu and Cov(x sub t, x sub t + k) = rho to the k power. We derive a prediction interval for x sub 2n + 1 based on the preceding 2n observations x sub 1, x sub 2 ..., x sub 2n. (Author).
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1989
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Aeronautics categories.
Long Run Behavior Of Interval Neutrosophic Markov Chain
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Author : D. Nagarajan
language : en
Publisher: Infinite Study
Release Date :
Long Run Behavior Of Interval Neutrosophic Markov Chain written by D. Nagarajan and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
As the world is a competitive one, prediction of the future trend is an important task for the survival of any organization. There are many statistical and technical methods available for doing this task and this can be done in an optimized way using Markov chain with time series where random changes are allowed. Markov chains are an essential technique in random process underlying the Markov property [1]. Longrun behavior is the behavior of the system where each and every input can be different and the free entry is unconditional. In addition, the cost of this behavior is the minimum of short run behavior.
Elements Of The Theory Of Markov Processes And Their Applications
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Author : A. T. Bharucha-Reid
language : en
Publisher: Courier Corporation
Release Date : 2012-04-26
Elements Of The Theory Of Markov Processes And Their Applications written by A. T. Bharucha-Reid and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-26 with Mathematics categories.
This graduate-level text and reference in probability, with numerous applications to several fields of science, presents nonmeasure-theoretic introduction to theory of Markov processes. The work also covers mathematical models based on the theory, employed in various applied fields. Prerequisites are a knowledge of elementary probability theory, mathematical statistics, and analysis. Appendixes. Bibliographies. 1960 edition.
Model Free Prediction And Regression
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Author : Dimitris N. Politis
language : en
Publisher: Springer
Release Date : 2015-11-13
Model Free Prediction And Regression written by Dimitris N. Politis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-13 with Mathematics categories.
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.
Topics In Stochastic Processes
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Author : Robert B. Ash
language : en
Publisher: Academic Press
Release Date : 2014-06-20
Topics In Stochastic Processes written by Robert B. Ash and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-20 with Mathematics categories.
Topics in Stochastic Processes covers specific processes that have a definite physical interpretation and that explicit numerical results can be obtained. This book contains five chapters and begins with the L2 stochastic processes and the concept of prediction theory. The next chapter discusses the principles of ergodic theorem to real analysis, Markov chains, and information theory. Another chapter deals with the sample function behavior of continuous parameter processes. This chapter also explores the general properties of Martingales and Markov processes, as well as the one-dimensional Brownian motion. The aim of this chapter is to illustrate those concepts and constructions that are basic in any discussion of continuous parameter processes, and to provide insights to more advanced material on Markov processes and potential theory. The final chapter demonstrates the use of theory of continuous parameter processes to develop the Itô stochastic integral. This chapter also provides the solution of stochastic differential equations. This book will be of great value to mathematicians, engineers, and physicists.
Technical Abstract Bulletin
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Author :
language : en
Publisher:
Release Date :
Technical Abstract Bulletin written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Science categories.
Markov Processes For Stochastic Modeling
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Author : Oliver Ibe
language : en
Publisher: Newnes
Release Date : 2013-05-22
Markov Processes For Stochastic Modeling written by Oliver Ibe and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-22 with Mathematics categories.
Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. Presents both the theory and applications of the different aspects of Markov processes Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.
Semi Markov Processes And Reliability
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Author : Nikolaos Limnios
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-02-16
Semi Markov Processes And Reliability written by Nikolaos Limnios and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-16 with Technology & Engineering categories.
At first there was the Markov property. The theory of stochastic processes, which can be considered as an exten sion of probability theory, allows the modeling of the evolution of systems through the time. It cannot be properly understood just as pure mathemat ics, separated from the body of experience and examples that have brought it to life. The theory of stochastic processes entered a period of intensive develop ment, which is not finished yet, when the idea of the Markov property was brought in. Not even a serious study of the renewal processes is possible without using the strong tool of Markov processes. The modern theory of Markov processes has its origins in the studies by A. A: Markov (1856-1922) of sequences of experiments "connected in a chain" and in the attempts to describe mathematically the physical phenomenon known as Brownian mo tion. Later, many generalizations (in fact all kinds of weakenings of the Markov property) of Markov type stochastic processes were proposed. Some of them have led to new classes of stochastic processes and useful applications. Let us mention some of them: systems with complete connections [90, 91, 45, 86]; K-dependent Markov processes [44]; semi-Markov processes, and so forth. The semi-Markov processes generalize the renewal processes as well as the Markov jump processes and have numerous applications, especially in relia bility.