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An Introduction to Stochastic Orders

An Introduction to Stochastic Orders
  • Author : Felix Belzunce,Carolina Martinez Riquelme,Julio Mulero
  • Publisher : Academic Press
  • Release : 29 September 2015
GET THIS BOOKAn Introduction to Stochastic Orders

An Introduction to Stochastic Orders discusses this powerful tool that can be used in comparing probabilistic models in different areas such as reliability, survival analysis, risks, finance, and economics. The book provides a general background on this topic for students and researchers who want to use it as a tool for their research. In addition, users will find detailed proofs of the main results and applications to several probabilistic models of interest in several fields, and discussions of fundamental properties

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An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
  • Author : Howard M. Taylor,Samuel Karlin
  • Publisher : Academic Press
  • Release : 10 May 2014
GET THIS BOOKAn Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study

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Introduction to Stochastic Dynamic Programming

Introduction to Stochastic Dynamic Programming
  • Author : Sheldon M. Ross
  • Publisher : Academic Press
  • Release : 10 July 2014
GET THIS BOOKIntroduction to Stochastic Dynamic Programming

Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist—providing counterexamples where appropriate—and then presents methods for

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Introduction to Stochastic Processes with R

Introduction to Stochastic Processes with R
  • Author : Robert P. Dobrow
  • Publisher : John Wiley & Sons
  • Release : 07 March 2016
GET THIS BOOKIntroduction to Stochastic Processes with R

An introduction to stochastic processes through the use of R Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The use of simulation, by means of the popular statistical freeware R, makes theoretical results come alive with practical, hands-on demonstrations. Written by a highly-qualified expert in the field, the author presents numerous examples from a wide

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Introduction to Stochastic Finance

Introduction to Stochastic Finance
  • Author : Jia-An Yan
  • Publisher : Springer
  • Release : 10 October 2018
GET THIS BOOKIntroduction to Stochastic Finance

This book gives a systematic introduction to the basic theory of financial mathematics, with an emphasis on applications of martingale methods in pricing and hedging of contingent claims, interest rate term structure models, and expected utility maximization problems. The general theory of static risk measures, basic concepts and results on markets of semimartingale model, and a numeraire-free and original probability based framework for financial markets are also included. The basic theory of probability and Ito's theory of stochastic analysis, as

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Introduction to Probability Models

Introduction to Probability Models
  • Author : Sheldon M. Ross
  • Publisher : Academic Press
  • Release : 11 December 2006
GET THIS BOOKIntroduction to Probability Models

Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing

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Introduction to Stochastic Processes

Introduction to Stochastic Processes
  • Author : Paul G. Hoel,Sidney C. Port,Charles J. Stone
  • Publisher : Waveland Press
  • Release : 01 December 1986
GET THIS BOOKIntroduction to Stochastic Processes

An excellent introduction for computer scientists and electrical and electronics engineers who would like to have a good, basic understanding of stochastic processes! This clearly written book responds to the increasing interest in the study of systems that vary in time in a random manner. It presents an introductory account of some of the important topics in the theory of the mathematical models of such systems. The selected topics are conceptually interesting and have fruitful application in various branches of

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Stochastic Processes

Stochastic Processes
  • Author : Peter Watts Jones,Peter Smith
  • Publisher : CRC Press
  • Release : 30 October 2017
GET THIS BOOKStochastic Processes

Based on a well-established and popular course taught by the authors over many years, Stochastic Processes: An Introduction, Third Edition, discusses the modelling and analysis of random experiments, where processes evolve over time. The text begins with a review of relevant fundamental probability. It then covers gambling problems, random walks, and Markov chains. The authors go on to discuss random processes continuous in time, including Poisson, birth and death processes, and general population models, and present an extended discussion on

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Combining Soft Computing and Statistical Methods in Data Analysis

Combining Soft Computing and Statistical Methods in Data Analysis
  • Author : Christian Borgelt,Gil González Rodríguez,Wolfgang Trutschnig,María Asunción Lubiano,María Angeles Gil,Przemyslaw Grzegorzewski,Olgierd Hryniewicz
  • Publisher : Springer Science & Business Media
  • Release : 12 October 2010
GET THIS BOOKCombining Soft Computing and Statistical Methods in Data Analysis

Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical

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An Introduction to Stochastic Processes and Their Applications

An Introduction to Stochastic Processes and Their Applications
  • Author : Petar Todorovic
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKAn Introduction to Stochastic Processes and Their Applications

This text on stochastic processes and their applications is based on a set of lectures given during the past several years at the University of California, Santa Barbara (UCSB). It is an introductory graduate course designed for classroom purposes. Its objective is to provide graduate students of statistics with an overview of some basic methods and techniques in the theory of stochastic processes. The only prerequisites are some rudiments of measure and integration theory and an intermediate course in probability

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An Introduction to the Geometry of Stochastic Flows

An Introduction to the Geometry of Stochastic Flows
  • Author : Fabrice Baudoin
  • Publisher : World Scientific
  • Release : 18 May 2022
GET THIS BOOKAn Introduction to the Geometry of Stochastic Flows

This book aims to provide a self-contained introduction to the local geometry of the stochastic flows associated with stochastic differential equations. It stresses the view that the local geometry of any stochastic flow is determined very precisely and explicitly by a universal formula referred to as the Chen-Strichartz formula. The natural geometry associated with the Chen-Strichartz formula is the sub-Riemannian geometry whose main tools are introduced throughout the text. By using the connection between stochastic flows and partial differential equations,

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An Introduction to Stochastic Differential Equations

An Introduction to Stochastic Differential Equations
  • Author : Lawrence C. Evans
  • Publisher : American Mathematical Soc.
  • Release : 11 December 2012
GET THIS BOOKAn Introduction to Stochastic Differential Equations

These notes provide a concise introduction to stochastic differential equations and their application to the study of financial markets and as a basis for modeling diverse physical phenomena. They are accessible to non-specialists and make a valuable addition to the collection of texts on the topic. --Srinivasa Varadhan, New York University This is a handy and very useful text for studying stochastic differential equations. There is enough mathematical detail so that the reader can benefit from this introduction with only

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Introduction to Stochastic Models

Introduction to Stochastic Models
  • Author : Roe Goodman
  • Publisher : Courier Corporation
  • Release : 18 May 1988
GET THIS BOOKIntroduction to Stochastic Models

Newly revised by the author, this undergraduate-level text introduces the mathematical theory of probability and stochastic processes. Using both computer simulations and mathematical models of random events, it comprises numerous applications to the physical and biological sciences, engineering, and computer science. Subjects include sample spaces, probabilities distributions and expectations of random variables, conditional expectations, Markov chains, and the Poisson process. Additional topics encompass continuous-time stochastic processes, birth and death processes, steady-state probabilities, general queuing systems, and renewal processes. Each section

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