Average customer rating:
- not enough
- Doubles as inexpensive textbook on operations research
- Schaum's Outline of Operations Research
- Solved problems book
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Schaum's Outline of Operations Research
Richard Bronson , and
Govindasami Naadimuthu
Manufacturer: McGraw-Hill
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ASIN: 0070080208 |
Book Description
Tackling the broad range of allocation problems that actually confront engineers, programmers and analysts in today's business and industrial worlds, this book takes readers step-by-step through all the mathematical programming techniques--including the trailblazing Karmarkar algorithm--needed to excel in any operations research course. It's easy to see why the first edition of this invaluable study guide sole more than 35,000 copies! It cuts down study time while it builds essential skills.
Customer Reviews:
not enough.......2007-01-23
First of all, be careful, this is not a text book. It has a good presentation for problem solution. First, there are a some solved problems, then supplementary problems are coming. The answers of supplementary problems are at the end of the book.
This book might be useful for beginners. For every topic, there are easy problems, not specific problems similar to case studies. If you are over beginner degree, this book will not be useful for you. Especially, integer programming sections are not satisfactory. For instance, there isn't any facility location problem solution.
To sum-up this book may be useful for a beginner as a workbook.
Doubles as inexpensive textbook on operations research.......2006-04-15
Some of the primary tools used by operations researchers are statistics, optimization, stochastics, queueing theory, game theory, graph theory, and simulation. Because of the computational nature of these fields operations research also has ties to computer science, and thus this outline is useful to people from both fields. OR is concerned with optimization problems in which one seeks to maximize or minimize a specific quantity. The first part of this book is on optimization via linear, integer, and nonlinear programming. Next, network analysis is covered. Network analysis is the general name given to certain specific techniques which can be used for the planning, management and control of projects. Two different techniques for network analysis were developed independently in the late 1950's - PERT (Program Evaluation and Review Technique) and CPM (Critical Path Management). These techniques are also covered in the outline. The next subject tackled is that of inventory models - allowing shortages, allowing price discounts, risk conditions, etc., and their mathematical modeling. Game theory, decision theory, and dynamic programming are all explained in the context of inventory models and forecasting. Finally, there is coverage of Markov chains and queueing theory. Queuing Theory arises from the use of mathematical analysis to theoretically describe production processes along with statistical/probabilistic techniques to account for varying dynamic patterns within the stages of a productive process. The problem to be met is simply entitled "congestion", what happens when a system does not operate smoothly or efficiently.
I really liked this Schaum's outline, and I used it to teach myself most of the mathematical processes covered without the need for any additional resources. The theory is given in small doses along with very illustrative examples. The mathematics starts with simple algebra and works up to nothing more complex than probability and statistics. I highly recommend it for anybody enrolled in an operations research class as well as computer scientists and mathematics students that are studying any subset of the topics covered in this book.
Schaum's Outline of Operations Research.......2002-07-21
i found this book to be a very helpfull tool a long with my text book.it excels in its simplicity and a wide varity of examples and solved problems written in plain english.
hope you like it too.
thank you.
M. Madain.
Solved problems book.......2001-06-13
Originally the book of Richard Bronson(1982) was very useful for the solution of simple problems, then a difficult one, but it is always required a text book accompany this. The students of my classes need all the time solved problems to practice.
Book Description
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation. Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application. Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains. Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.
Customer Reviews:
Okay........2005-05-06
First, I'll like to comment on the termiology. I'm PhD specializing in stochastic simulation in operations researcn and I've found the book is written in a language that's not quite standard (it might have something to do with his background in Statistics). Some people may argue that "names" are just "names" but it could cause confusion. And, in the chapter of stochastic approximation, the author failed to mention a couple of well-known existing methodology (somehow show a poor literature review in the field.) Strong emphasis has been given on importance sampling on that particular chapter, but author failed to mention in what context will importance sampling work. If you assume Bayesian approach and have prior on the parameters, then it works. But, if you're a frequentist, it's not necessarily working for your model.
Going back to the first chapter, I found the construction of MCMC is presented much more clearly in Sheldon Ross's Probability Model rather than this book.
great collection of articles on applications.......2000-12-20
Gilks, Richardson and Spiegelhalter edited this marvelous collection of papers on applications of Markov Chain Monte Carlo methods. There has been a big payoff for Bayesians as this method has been a breakthrough for dealing with flexible prior distributions. Most (but not all) of the articles deal with Bayesian applications. The editors themselves start out with an introductory chapter that covers the basic ideas and sets the stage for the articles to come. They provide many references including several of the articles in this volume.
The list of authors is quite impressive and many interesting examples are presented. The editors themselves contribute to other chapters. Spiegelhalter and Gilks co-authored a chapter on a Hepatitis B case study with Best and Inskip. Gilks has a chapter on full conditional distributions and co-authors a chapter on strategies for improving the MCMC algorithms. Richardson contributes a chapter on measurement error.
George and McCulloch deal with the use of Gibbs sampling to choose variables in a model based on a Bayesian approach. Raftery also has a chapter on Bayesian approaches in hypothesis testing and model selection. Green covers image analysis. There are many others (25 chapters in all). This is a great reference for anyone interested in MCMC methods.
The BUGS (Bayesian inference Using Gibbs Sampling)software was developed by Spiegelhalter, Thomas, Best and Gilks to implement Gibbs sampling in a variety of contexts. They illustrate its use along with the diagnostic software CODA in the application in Chapter 2. It is also mentioned in various other chapters in the book. There is currently a version called winBUGS which is designed for Windows operating systems.
Before jumping into the use of MCMC a user would be well advised to study this book.
Very Useful........1997-10-25
We recommend this book to anyone who is interested in learning MCMC methods. Contains a excellent selection of practical examples. Christopher Gordon and Steve Hirschowitz
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Markov Chains and Stochastic Stability (Communications and Control Engineering)
Sean P. Meyn , and
Richard L. Tweedie
Manufacturer: Springer
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ASIN: 3540198326 |
Book Description
Markov Chains and Stochastic Stability is part of the Communications and Control Engineering Series (CCES) edited by Professors B.W. Dickinson, E.D. Sontag, M. Thoma, A. Fettweis, J.L. Massey and J.W. Modestino. The area of Markov chain theory and application has matured over the past 20 years into something more accessible and complete. It is of increasing interest and importance. This publication deals with the action of Markov chains on general state spaces. It discusses the theories and the use to be gained, concentrating on the areas of engineering, operations research and control theory. Throughout, the theme of stochastic stability and the search for practical methods of verifying such stability, provide a new and powerful technique. This does not only affect applications but also the development of the theory itself. The impact of the theory on specific models is discussed in detail, in order to provide examples as well as to demonstrate the importance of these models.
Markov Chains and Stochastic Stability can be used as a textbook on applied Markov chain theory, provided that one concentrates on the main aspects only. It is also of benefit to graduate students with a standard background in countable space stochastic models. Finally, the book can serve as a research resource and active tool for practitioners.
Book Description
Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians. This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. In particular, the introductory coverage of random variable generation has been totally revised, with many concepts being unified through a fundamental theorem of simulation
There are five completely new chapters that cover Monte Carlo control, reversible jump, slice sampling, sequential Monte Carlo, and perfect sampling. There is a more in-depth coverage of Gibbs sampling, which is now contained in three consecutive chapters. The development of Gibbs sampling starts with slice sampling and its connection with the fundamental theorem of simulation, and builds up to two-stage Gibbs sampling and its theoretical properties. A third chapter covers the multi-stage Gibbs sampler and its variety of applications. Lastly, chapters from the previous edition have been revised towards easier access, with the examples getting more detailed coverage.
This textbook is intended for a second year graduate course, but will also be useful to someone who either wants to apply simulation techniques for the resolution of practical problems or wishes to grasp the fundamental principles behind those methods. The authors do not assume familiarity with Monte Carlo techniques (such as random variable generation), with computer programming, or with any Markov chain theory (the necessary concepts are developed in Chapter 6). A solutions manual, which covers approximately 40% of the problems, is available for instructors who require the book for a course.
Christian P. Robert is Professor of Statistics in the Applied Mathematics Department at Université Paris Dauphine, France. He is also Head of the Statistics Laboratory at the Center for Research in Economics and Statistics (CREST) of the National Institute for Statistics and Economic Studies (INSEE) in Paris, and Adjunct Professor at Ecole Polytechnique. He has written three other books, including The Bayesian Choice, Second Edition, Springer 2001. He also edited Discretization and MCMC Convergence Assessment, Springer 1998. He has served as associate editor for the Annals of Statistics and the Journal of the American Statistical Association. He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the Societié de Statistique de Paris in 1995.
George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. He has served as the Theory and Methods Editor of the Journal of the American Statistical Association and Executive Editor of Statistical Science. He has authored three other textbooks: Statistical Inference, Second Edition, 2001, with Roger L. Berger; Theory of Point Estimation, 1998, with Erich Lehmann; and Variance Components, 1992, with Shayle R. Searle and Charles E. McCulloch. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and an elected fellow of the International Statistical Institute.
Customer Reviews:
Comprehensive but hard to read.......2007-10-03
There is no doubts this text is a comprehensive study of Monte Carlo methods with an impressive number of examples. However, I must say it is hard to read for someone who is beginning to work with Monte Carlo methods. I highly recommend the book by Sobol (A primer for the Monte Carlo Method) which I think it remains to be the best introduction to the subject. After reading and enjoying this primer you will be ready to take full advantage of Robert and Casella's book.
Comprehensive and detailed.......2006-04-08
I own both versions of this book. The authors have made significant amount of changes and enrichments in the second edition. Many recent developments in this field, such as perfect sampling, trans-dimensional MCMC and sequential Monte Carlo are covered in certain details. The level of this book is intermediate to advanced, and I used this book for the 3rd year Ph.D. students. My only disappointment is the examples are not up to my expectation. However, the problems at the back of each chapter include some interesting applications.
I highly recommend this book to anyone who wants to understand and apply MCMC and other Monte Carlo methods.
Monte Carlo Statistical Methods (by Christian P. Robert).......2006-03-20
It is a fantastic book for Monte Carlo Methods
Review of the Monte Carlo Statistical Methods book.......2006-03-02
A good book, with a really interesting mathematical treatement to different simulation techniques, but a little bit complicated in some aspects.
Does something necessary, does it well........2002-12-10
This text may or may not be the best book on MC for a particular application; to be honest, it's the only book on MC I own.
However, I did peruse a number of texts before I bought this one, and I am very pleased with my decision. To me, this book does something that seems necessary but is relatively uncommon: it gives a detailed, modern, comprehensive introduction to MC methods per se. There are other texts that might have one of those characteristics, but they seem to either not have all of them: they either are not modern, not comprehensive, not introductory, or are not concerned with Monte Carlo per se.
Many other excellent texts, for example, are largely oriented toward Bayesian implementations, or general integration, but not both.
I would highly recommend this book as an excellent introduction to MC methods as a general computational tool.
Book Description
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.
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Random Iterative Models (Stochastic Modelling and Applied Probability)
Marie Duflo
Manufacturer: Springer
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ASIN: 3540571000 |
Book Description
The recent development of computation and automation has lead to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multiaccess broadcast channels, self-learning of neural networks ...). This book provides an up-to-date view of a wide range of those methods: stochastic approximation, linear and non-linear models, controlled Markov chains, estimation and adaptive control, learning ...Mathematicians (researchers and also students) and engineers will find here a self-contained account of many approaches to those theories.
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Topics in the Constructive Theory of Countable Markov Chains
G. Fayolle ,
V. A. Malyshev , and
M. V. Menshikov
Manufacturer: Cambridge University Press
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ASIN: 0521461979 |
Book Description
Markov chains are an important idea, related to random walks, which crops up widely in applied stochastic analysis. They are used for example in performance modeling and evaluation of computer networks, queuing networks, and telecommunication systems. The main point of the present book is to provide methods, based on the construction of Lyapunov functions, of determining when a Markov chain is ergodic, null recurrent, or transient. These methods, which are on the whole original and new, can also be extended to the study of questions of stability. Of particular concern are reflected random walks and reflected Brownian motion. Here, the authors provide a self-contained introduction to the theory and details of how the required Lyapunov functions are constructed in various situations.
Book Description
MARKOV CHAINS: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.
The book consists of eight chapters. Chapter 1 is a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory is also discussed. Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chains for computing. Chapter 3 studies re-manufacturing systems and presents Markovian models for reverse manufacturing applications. In Chapter 4, Hidden Markov models are applied to classify customers. Chapter 5 discusses the Markov decision process for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. Chapter 6 covers higher-order Markov chain models. Multivariate Markov models are discussed in Chapter 7. It presents a class of multivariate Markov chain models with a lower order of model parameters. Chapter 8 studies higher-order hidden Markov models. It proposes a class of higher-order hidden Markov models with an efficient algorithm for solving the model parameters.
This book is aimed at students, professionals, practitioners, and researchers in applied mathematics, scientific computing, and operational research, who are interested in the formulation and computation of queueing and manufacturing systems.
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Queuing Theory and Telecommunications: Networks and Applications
Giovanni Giambene
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Modeling and Analysis of Telecommunications Networks
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Monte Carlo Statistical Methods (Springer Texts in Statistics)
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The Elements of Statistical Learning
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All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
ASIN: 0387240659 |
Book Description
Queuing Theory and Telecommunications: Networks and Applications provides some fundamental knowledge in queuing theory, as well as essential analytical methods and approaches to be employed to evaluate and design telecommunication networks.
This work provides methods for teletraffic analysis as well as descriptions of current network technologies such as ISDN, B-ISDN, IP-based networks, MPLS, GMPLS, NGN and local access systems, including ADSL-based, Ethernet, Token Passing, and WiFi. Also, numerous solved exercises are provided in order to illustrate the applications of queuing theory in telecommunication networks. The following advanced telecommunication problems are modeled and solved by means of queuing analysis: statistics of the transmission delay for packet data traffic arriving at a transmission buffer; blocking behavior for bursty call arrival processes; characterization of Markovian traffic sources; performance of traffic regulators, analysis of access protocols and more. The author provides readers with a correct understanding of fundamental methods to be applied in the analysis of telecommunications systems.
Queuing Theory and Telecommunications: Networks and Applications is a reference text for advanced undergraduate and graduate level courses in telecommunications engineering and networking. It will also serve as a useful work for system engineers involved in network dimensioning.
Amazon.com
This text is a classic in probability, statistics, and estimation and in the application of these fields to modern engineering problems. Probability, Random Variables, and Stochastic Processes assumes a strong college mathematics background. The first half of the text develops the basic machinery of probability and statistics from first principles while the second half develops applications of the basic theory. Topics in the first section include probability distributions and densities, random variables and vectors, expectations, covariance, correlations, functions of random variables and vectors, and conditional distributions and densities. In this third edition of the text, the second half of the book has been substantially updated and expanded to include new or revised discussions of the following topics: mean square estimation, likelihood tests, maximum entropy methods, Monte Carlo techniques, spectral representations and estimation, sampling theory, bispectra and system identification, cyclostationary processes, deterministic signals in noise, and the Wiener and Kalman filters. Probability, Random Variables, and Stochastic Processes covers a remarkable density of material and the clarity of both presentation and notation make this book invaluable as a text and a reference.
Book Description
The fourth edition of Probability, Random Variables and Stochastic Processes has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest. Approximately 1/3 of the text is new material--this material maintains the style and spirit of previous editions. In order to bridge the gap between concepts and applications, a number of additional examples have been added for further clarity, as well as several new topics.
Customer Reviews:
Papoulis should never be allowed to write a book again.......2006-11-29
Absolutely fails as a text book. This book offers absolutely no flow whatsoever for a student to follow. Papoulis didn't write this book for a student, he wrote it for a peer to discuss over tea to showcase how much he can squeeze into 800 pages. Apprently, quality over quantity means absolutely nothing to this guy.
If you want a good textbook on random variables, try Peebles. Each topic in Peebles is coherently presented followed by a numerical example that's easily understood.
Yes this book covers alot of ground, but it does a horrible job explaining just about everything it covers.
Not for the beginner.......2006-09-09
I bought this book because it is a classic. It is hard reading, the style is dry and somewhat bloated, a combination that makes you dislike the author immediately. That is a shame, for the author is clearly very knowledgeable. If only he had tried to be a bit friendlier, try to TEACH instead of PREACH...
Definitely NOT for beginners, not even for intermediate level readers.
Very formal treatment, use this if you also have a tutor / professor.......2006-03-20
Im writing this review because I felt most of the other reviews here were unfair. If you are not interested in the subject, you'll find any text on random processes bad!
Anyway, the text book is a very formal treatment of the subject - and is a good accompaniment if you have access to a professor or tutor you can discuss things over with. If you are planning to read this up on your own, it's hard. The exercises are good and must be attempted to understand the subject. Get a solutions manual from somewhere to verify your work.
I hate probability now; don't even mention random variables.......2006-02-13
Oh my goodness, what did I get myself into. I had taken a statistics and probability class before and breezed through it no problem. A+ for the class, and I even missed some classes. So I decided I should take a higher level course in probability and random variables. Numbers are fun, right? WRONG. This book will completely turn you off to anything related to probability and random variables, if you are a beginner. If you don't have an intrinsic love for probability and random variables already, then I recommend choosing another book. Basically what is wrong with this book has already been outlined by the reviewers that gave this one 1 or 2 stars. Difficulty in comprehending mainly because:
1) Barely any practical applications (e.g. number examples) A lot of the examples in the book and even the problems will present you a "general" problem to solve, meaning no numerical values, but 'k' and 'n' and such. Very abstract!
2) Errors. I didn't even know there were errors in this book until my instructor pointed them out. Heck, how are you supposed to learn if you don't spot the errors and take them as truth!
3) Pre-bed time reading material. Very boring and dry like another reviewer has said. I cannot fathom anyone enjoying a page of this book. Dictionaries are more fun to read; at least I would take something with me. My own eagerness to do well in class is stifled by this book, making me ask myself why I must endure this torture.
This book is too advanced for beginners, and will not promote healthy learning of the material. Please read a few of the low star reviews for better alternatives. If you must take a class that uses this book, I suggest paying attention 100% in class and prayer. My instructor uses slides from Pillai, and even those had errors in it. I am glad to hear I am not alone on this one.
Choose another book.......2005-10-26
Very poorly written unless you already understand the topic.
Book is heavy on poorly explained theory and provides very little in the way of practical applications or examples.
Far too many errors that even the errata card doesn't address.
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