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Statistical Inference For Piecewise Deterministic Markov Processes
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Book Synopsis Statistical Inference for Piecewise-deterministic Markov Processes by : Romain Azais
Download or read book Statistical Inference for Piecewise-deterministic Markov Processes written by Romain Azais and published by John Wiley & Sons. This book was released on 2018-07-30 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance, neuroscience, networks, finance... Such processes are defined by a deterministic motion punctuated by random jumps at random times, and offer simple yet challenging models to study. Nevertheless, the issue of statistical estimation of the parameters ruling the jump mechanism is far from trivial. Responding to new developments in the field as well as to current research interests and needs, Statistical inference for piecewise-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as well as applied models. The present book also dwells on statistics in the context of Markov chains, since piecewise-deterministic Markov processes are characterized by an embedded Markov chain corresponding to the position of the process right after the jumps.
Book Synopsis Statistical Inference for Markov Processes by : Patrick Billingsley
Download or read book Statistical Inference for Markov Processes written by Patrick Billingsley and published by . This book was released on 1961 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes by : Benoîte de Saporta
Download or read book Numerical Methods for Simulation and Optimization of Piecewise Deterministic Markov Processes written by Benoîte de Saporta and published by John Wiley & Sons. This book was released on 2016-01-26 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mark H.A. Davis introduced the Piecewise-Deterministic Markov Process (PDMP) class of stochastic hybrid models in an article in 1984. Today it is used to model a variety of complex systems in the fields of engineering, economics, management sciences, biology, Internet traffic, networks and many more. Yet, despite this, there is very little in the way of literature devoted to the development of numerical methods for PDMDs to solve problems of practical importance, or the computational control of PDMPs. This book therefore presents a collection of mathematical tools that have been recently developed to tackle such problems. It begins by doing so through examples in several application domains such as reliability. The second part is devoted to the study and simulation of expectations of functionals of PDMPs. Finally, the third part introduces the development of numerical techniques for optimal control problems such as stopping and impulse control problems.
Book Synopsis Piecewise-deterministic Markov Processes by : M. H. A. Davis
Download or read book Piecewise-deterministic Markov Processes written by M. H. A. Davis and published by . This book was released on 1984 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Markov Models & Optimization by : M.H.A. Davis
Download or read book Markov Models & Optimization written by M.H.A. Davis and published by Routledge. This book was released on 2018-02-19 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system models. A PDP is a Markov process that follows deterministic trajectories between random jumps, the latter occurring either spontaneously, in a Poisson-like fashion, or when the process hits the boundary of its state space. This formulation includes an enormous variety of applied problems in engineering, operations research, management science and economics as special cases; examples include queueing systems, stochastic scheduling, inventory control, resource allocation problems, optimal planning of production or exploitation of renewable or non-renewable resources, insurance analysis, fault detection in process systems, and tracking of maneuvering targets, among many others. The first part of the book shows how these applications lead to the PDP as a system model, and the main properties of PDPs are derived. There is particular emphasis on the so-called extended generator of the process, which gives a general method for calculating expectations and distributions of system performance functions. The second half of the book is devoted to control theory for PDPs, with a view to controlling PDP models for optimal performance: characterizations are obtained of optimal strategies both for continuously-acting controllers and for control by intervention (impulse control). Throughout the book, modern methods of stochastic analysis are used, but all the necessary theory is developed from scratch and presented in a self-contained way. The book will be useful to engineers and scientists in the application areas as well as to mathematicians interested in applications of stochastic analysis.
Book Synopsis Statistical Inference for Markov Processes, Reprinted by : Patrick Billingsley
Download or read book Statistical Inference for Markov Processes, Reprinted written by Patrick Billingsley and published by . This book was released on 1974 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Topics and Stochastic Models for Dependent Data with Applications by : Vlad Stefan Barbu
Download or read book Statistical Topics and Stochastic Models for Dependent Data with Applications written by Vlad Stefan Barbu and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.
Book Synopsis Statistical Inference for Markov Processes by : Walter F. Johnson
Download or read book Statistical Inference for Markov Processes written by Walter F. Johnson and published by . This book was released on 1961 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Markov Chain Monte Carlo by : Dani Gamerman
Download or read book Markov Chain Monte Carlo written by Dani Gamerman and published by CRC Press. This book was released on 2006-05-10 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.
Book Synopsis Piecewise Deterministic Markov Processes by : Taleb Salameh Odeh Alkurdi
Download or read book Piecewise Deterministic Markov Processes written by Taleb Salameh Odeh Alkurdi and published by . This book was released on 2013 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Markov Chain Monte Carlo by : Dani Gamerman
Download or read book Markov Chain Monte Carlo written by Dani Gamerman and published by CRC Press. This book was released on 1997-10-01 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists.
Book Synopsis Statistical Inference for Markov Processes C.Patrick Billingsley by : Patrick Billingsley
Download or read book Statistical Inference for Markov Processes C.Patrick Billingsley written by Patrick Billingsley and published by . This book was released on 1961 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Inferences for Markov Processes by : Patrick Billingsley
Download or read book Statistical Inferences for Markov Processes written by Patrick Billingsley and published by . This book was released on 1961 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Inference in Hidden Markov Models by : Olivier Cappé
Download or read book Inference in Hidden Markov Models written by Olivier Cappé and published by Springer Science & Business Media. This book was released on 2006-04-12 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
Book Synopsis Statistical Inference on Aggregated Markov Processes by : Wenyu Wang
Download or read book Statistical Inference on Aggregated Markov Processes written by Wenyu Wang and published by . This book was released on 1989 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Some Aspects of Statistical Inference for M-th Order Markov Processes by : Ramanpillai Krishna Pillai
Download or read book Some Aspects of Statistical Inference for M-th Order Markov Processes written by Ramanpillai Krishna Pillai and published by . This book was released on 1963 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contributions to the Theory of Piecewise Deterministic Markov Processes and Applications to Generalized Age Processes and Storage Models by : Andreas Löpker
Download or read book Contributions to the Theory of Piecewise Deterministic Markov Processes and Applications to Generalized Age Processes and Storage Models written by Andreas Löpker and published by . This book was released on 2005 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
