Understanding Markov Chains
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Understanding Markov Chains
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Author : Nicolas Privault
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-13
Understanding Markov Chains written by Nicolas Privault 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 2013-08-13 with Mathematics categories.
This book provides an undergraduate introduction to discrete and continuous-time Markov chains and their applications. A large focus is placed on the first step analysis technique and its applications to average hitting times and ruin probabilities. Classical topics such as recurrence and transience, stationary and limiting distributions, as well as branching processes, are also covered. Two major examples (gambling processes and random walks) are treated in detail from the beginning, before the general theory itself is presented in the subsequent chapters. An introduction to discrete-time martingales and their relation to ruin probabilities and mean exit times is also provided, and the book includes a chapter on spatial Poisson processes with some recent results on moment identities and deviation inequalities for Poisson stochastic integrals. The concepts presented are illustrated by examples and by 72 exercises and their complete solutions.
Markov Chains
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Author : J. R. Norris
language : en
Publisher: Cambridge University Press
Release Date : 1998-07-28
Markov Chains written by J. R. Norris and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-07-28 with Mathematics categories.
Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.
Markov Models
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Author : Robert Tier
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-03-12
Markov Models written by Robert Tier and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-12 with Machine learning categories.
Discover How to Master Unsupervised Machine Learning and Crack Some of the Greatest Data Enigmas With Markov Models! Would you like to unlock the mysteries of Data Science? Are you yearning to understand how to make educated predictions on the weather, horse races, your unborn baby's facial features, or your boss's next black mood? Would you like a guide to explain these and many other "phenomenons" in clear, easy-to-understand language? If the answer is 'yes' then you'll want to Download this book today! It's never been easier to make predictions and smart analysis with the use of Markov Models. You don't need a crystal ball or any wizardry. The only thing you need is science, some average high-school math skills and a decent knowledge of Python programming in order to solve the most perplexing problems. And if you're unfamiliar with Python programming or Machine learning, don't worry, it'll all be explained in this book. Inside this book I'm going to show you how to be a data master. You'll discover how to solve almost-unsolvable machine learning problems in no time. I'm going to show you the tools, code, and methods needed to effectively use Markov Models for any event or situation you come across. Download This Book Today and Discover: How to program with Python The secrets behind unsupervised machine learning How to use Markov Models to master machine learning How to make predictions with Markov Models How to use Markov Chains How to use Hidden Markov Models The 3 main problems of Markov Models and how to overcome them How to use Python to find the probability of longer and more complex problems What packages to get for using Python for Markov Models How to implement HMM algorithms How to build a speech recognizer A code that will turn gibberish into understandable text How to forecast the weather The secrets behind Queueing Theory The Markov Mutation Model The Secret Structure of Google's PageRank Algorithm How to perform Google PageRank in PythonAnd much, much more! So save yourself some time and frustration trying to learning these intricate algorithms on your own. Let me help you get started quickly and easily. Download Markov Models today and Enjoy Mastering Data Science!
Markov Chains
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Author : David Freedman
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Markov Chains written by David Freedman 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 2012-12-06 with Mathematics categories.
A long time ago I started writing a book about Markov chains, Brownian motion, and diffusion. I soon had two hundred pages of manuscript and my publisher was enthusiastic. Some years and several drafts later, I had a thousand pages of manuscript, and my publisher was less enthusiastic. So we made it a trilogy: Markov Chains Brownian Motion and Diffusion Approximating Countable Markov Chains familiarly - MC, B & D, and ACM. I wrote the first two books for beginning graduate students with some knowledge of probability; if you can follow Sections 10.4 to 10.9 of Markov Chains you're in. The first two books are quite independent of one another, and completely independent of the third. This last book is a monograph which explains one way to think about chains with instantaneous states. The results in it are supposed to be new, except where there are specific disclaim ers; it's written in the framework of Markov Chains. Most of the proofs in the trilogy are new, and I tried hard to make them explicit. The old ones were often elegant, but I seldom saw what made them go. With my own, I can sometimes show you why things work. And, as I will VB1 PREFACE argue in a minute, my demonstrations are easier technically. If I wrote them down well enough, you may come to agree.
A Markov Chain Model For Projecting School Enrollments
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Author : Gary Arthur Mohrenweiser
language : en
Publisher:
Release Date : 1970
A Markov Chain Model For Projecting School Enrollments written by Gary Arthur Mohrenweiser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1970 with Markov processes categories.
Martingales And Markov Chains
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Author : Paolo Baldi
language : en
Publisher: CRC Press
Release Date : 2002-04-26
Martingales And Markov Chains written by Paolo Baldi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-04-26 with Mathematics categories.
A thorough grounding in Markov chains and martingales is essential in dealing with many problems in applied probability, and is a gateway to the more complex situations encountered in the study of stochastic processes. Exercises are a fundamental and valuable training tool that deepen students' understanding of theoretical principles and prepare th
Approximating Countable Markov Chains
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Author : David Freedman
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Approximating Countable Markov Chains written by David Freedman 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 2012-12-06 with Mathematics categories.
A long time ago I started writing a book about Markov chains, Brownian motion, and diffusion. I soon had two hundred pages of manuscript and my publisher was enthusiastic. Some years and several drafts later, I had a thousand pages of manuscript, and my publisher was less enthusiastic. So we made it a trilogy: Markov Chains Brownian Motion and Diffusion Approximating Countable Markov Chains familiarly - MC, B & D, and ACM. I wrote the first two books for beginning graduate students with some knowledge of probability; if you can follow Sections 10.4 to 10.9 of Markov Chains, you're in. The first two books are quite independent of one another, and completely independent of this one, which is a monograph explaining one way to think about chains with instantaneous states. The results here are supposed to be new, except when there are specific disclaimers. It's written in the framework of Markov chains; we wanted to reprint in this volume the MC chapters needed for reference. but this proved impossible. Most of the proofs in the trilogy are new, and I tried hard to make them explicit. The old ones were often elegant, but I seldom saw what made them go. With my own, I can sometimes show you why things work. And, as I will argue in a minute, my demonstrations are easier technically. If I wrote them down well enough, you may come to agree.
Markov Chains
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Author : J. R. Norris
language : en
Publisher: Cambridge University Press
Release Date : 1998-07-28
Markov Chains written by J. R. Norris and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-07-28 with Mathematics categories.
Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.
Markov Chains
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Author : Dean L. Isaacson
language : en
Publisher: John Wiley & Sons
Release Date : 1976-03-05
Markov Chains written by Dean L. Isaacson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1976-03-05 with Mathematics categories.
Fundamental concepts of Markov chains; The classical approach to markov chains; The algebraic approach to Markov chains; Nonstationary Markov chains and the ergodic coeficient; Analysis of a markov chain on a computer; Continuous time Markov chains.
Markov Chains
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Author : Randal Douc
language : en
Publisher: Springer
Release Date : 2018-12-11
Markov Chains written by Randal Douc and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-11 with Mathematics categories.
This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an overview of the literature. Part I lays the foundations of the theory of Markov chain on general states-space. Part II covers the basic theory of irreducible Markov chains on general states-space, relying heavily on regeneration techniques. These two parts can serve as a text on general state-space applied Markov chain theory. Although the choice of topics is quite different from what is usually covered, where most of the emphasis is put on countable state space, a graduate student should be able to read almost all these developments without any mathematical background deeperthan that needed to study countable state space (very little measure theory is required). Part III covers advanced topics on the theory of irreducible Markov chains. The emphasis is on geometric and subgeometric convergence rates and also on computable bounds. Some results appeared for a first time in a book and others are original. Part IV are selected topics on Markov chains, covering mostly hot recent developments.