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:

Download or read book Statistical Inference about Markov Chains written by Sai-Sing Lin and published by . This book was released on 1966 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference in Markov Chains Using the Principal of Minimum Discrimination Information written by Said Mohamed Rujbani and published by . This book was released on 1979 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference about Markov Chains written by T.W. Anderson and published by . This book was released on 1957 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 2018-07-31 with total page 279 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.

Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.

Download or read book Some Aspects of Statistical Inference in Markov Chains written by and published by . This book was released on 1976 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Stochastic Epidemic Models with Inference written by Tom Britton and published by Springer Nature. This book was released on 2019-11-30 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.

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.

Download or read book Micro and Macro Data in Statistical Inference on Markov Chains written by Gunnar Rosenqvist and published by . This book was released on 1986 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference in Stochastic Processes written by N.U. Prabhu and published by CRC Press. This book was released on 2020-08-13 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di

Download or read book Exact Statistical Inference on Markov Chain Models written by Timothy Duane Johnson and published by . This book was released on 1997 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference for Real valued Markov Chains and Some Applications written by Gregorio Saravia Atuncar and published by . This book was released on 1994 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tools for Statistical Inference written by Martin A. Tanner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. In this second edition, I have attempted to expand the treatment of many of the techniques dis cussed, as well as include important topics such as the Metropolis algorithm and methods for assessing the convergence of a Markov chain algorithm. Prerequisites for this book include an understanding of mathematical statistics at the level of Bickel and Doksum (1977), some understanding of the Bayesian approach as in Box and Tiao (1973), experience with condi tional inference at the level of Cox and Snell (1989) and exposure to statistical models as found in McCullagh and Neider (1989). I have chosen not to present the proofs of convergence or rates of convergence since these proofs may require substantial background in Markov chain theory which is beyond the scope ofthis book. However, references to these proofs are given. There has been an explosion of papers in the area of Markov chain Monte Carlo in the last five years. I have attempted to identify key references - though due to the volatility of the field some work may have been missed.

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.

Download or read book Combining Micro and Macro Data in Statistical Inference about Markov Chains written by Gunnar Rosenqvist and published by . This book was released on 1981 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference from Dependent Data written by Sai Nishanth Dikkala and published by . This book was released on 2020 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, the study of high-dimensional probability has taken centerstage within many research communities including Computer Science, Statistics and Machine Learning. Very often, due to the process according to which data is collected, the samples in a dataset have implicit correlations amongst them. Such correlations are commonly ignored as a first approximation when trying to analyze statistical and computational aspects of an inference task. In this thesis, we explore how to model such dependences between samples using structured high-dimensional distributions which result from imposing a Markovian property on the joint distribution of the data, namely Markov Random Fields (MRFs) and Markov chains. On MRFs, we explore a quantification for the amount of dependence and we strengthen previously known measure concentration results under a certain weak dependence condition on an MRF called the high-temperature regime. We then go on to apply our novel measure concentration bounds to improve the accuracy of samples computed according to a certain Markov Chain Monte Carlo procedure. We then show how to extend some classical results from statistical learning theory on PAC-learnability and uniform convergence to training data which is dependent under the high temperature condition. Then, we explore the task of regression on data which is dependent according to an MRF under a stronger amount of dependence than is allowed by the high-temperature condition. We then shift our focus to Markov chains where we explore the question of testing whether a certain trajectory we observe corresponds to a chain P or not. We discuss what is a reasonable formulation of this problem and provide a tester which works without observing a trajectory whose length contains multiplicative factors of the mixing or covering time of the chain P. We finally conclude with some broad directions for further research on statistical inference under data dependence.