A great -and gentle - introduction to Graph Theory... clear definitions and examples, great figures, useful exercises, and even some clever quotes. Everything you could ask for - if only all texts were this clear and well-organized. This was my first foray into the topic, and Wilson's text made it enjoyable.

Because graphic methods are very popular in statistics, when you read the title you might think this is a book on the use of graphics in statistics. That is not what the book is about. The directed graph on the cover might be a hint for some. The book deals with the theory of undirected and directed graphs which has applications to causal modeling in statistics and the development of expert systems (which Edwards claim are now more commonly referred to as probabilistic networks).

This subject is being made popular again based on the recent work of Edwards, Pearl and a few others. The book incorporate the approach in many classical statistical problems. This is not commonly seen except in specialized texts on latent variable models.

Edwards discusses implementation of the methods with the freeware MIMS that is available in Denmark and on the web. The book is very well written and applications in MIMS are given throughout the text. Edwards also provides us with an excellent list of references (over 200 with many on causal modeling).

The software LISREL produced by researchers in the US at UCLA for latent variable and path analyses is only briefly mentioned on page 217. The lack of coverage of American and British publications on this topic is the only drawback I see.

This is an excellent book for anyone who wishes to not only understand the theory behind latent growth curve modeling but also seeing how it is directly applied in a number of situations. For a reader like me who depends upon the literature to help understand newer statistical approaches, a book like this is a breath of fresh air. The book presents very clearly how to set up a basic LGC model and includes other topics such as dealing with missing data, interaction effects and multilevel approaches to longitudinal data analysis. The appendix contains a number of example LGCM models in the software language of EQS, LISREL and AMOS. I most highly recommend this text for beginners and more advanced modelers alike!

This is one of the best books on the market for a course on linear regression analysis. It contains everything from the basic least squares equations to problems of multicollinearity, and a section on non-linear regression is also thrown in for good measure. Not a cheap book (Wiley would do well to consider inexpensive paperback editions for non-plutocrats)but highly recommended nevertheless. The book contains many applications to real-life problems and questions are included at the ends of the chapters, making this a valuable textbook for a very wide range of students.

Matrix Analytic Methods (MAM) are great modeling tools that can analyze a variety of stochastic systems in a unified way and in an algorithmically tractable manner. This book is one of the greatest that have been published on queueing theory and stochastic modeling.
This book covers
1. Examples of quasi-birth-and-death (QBD) processes.
2. Phase-type distributions
3. Stationary and non-stationary QBDs
4. Algorithms (from probabilistic reasoning)
5. And many others.
I used this book as a text for my graduate students and it was a real pleasure. Throughout the books, readers will see MAM as an art of modeling and analysis of stochastic systems, not the pile of techniques.
This book is aiming at readers in queueing theory, computer science, OR/MS, statistics, mathematics, and many other related disciplines. This book also serves as the source of bibliography not otherwise easily available.

A good variety of problems, easy, medium, and hard. I was able to read through the chapters and understand the mathematics. The computer programs truly complement the sections.

This book provides an excellent introduction to Probability Theory.

It only requires high school algebra and calculus.

It covers both discrete and continous probabilities.

It has an exceptionally clear discussion of distribution functions and how they are used to define discrete and continous probabilities.

Its discussion of Random Variables is also very clear.

While the book requires knowledge of Calculus, it is for the absolute beginner in Probability Theory. It does not assume any prior knowlege of the subject.

The explanations are clear and easy to follow.

The exercises are useful and help establish mastery of the subject.

The book is eminently suitable for college students after they have had their first calculus course.

Understanding probability theory is important to a number of disciplines, both in science and business. This book is not just written for those who aspire to be mathematicians but those who need to have an understanding of probability theory in non-mathematical careers.

Starting from the basic Probability concepts presented in an unformal (yet rigourous) way, this book moves swiftly on to some of the central concepts and results of the theory of stochastic processes. Basic (but fundamental) concepts like stochastic convergence, stationarity, stochastic differentiation and integration are presented in a rigourous yet mathematically accessible way. The latter chapters focus on some of the most applicable parts of the theory of stochastic processes:
Response of linear systems to stochastic inputs, Gaussean processes, the Wiener process, stochastic differential equations and response of non-linear systems.
The pre-requisites are exceptionally low: basic calculus and matrix theory are sufficient to get you going. However I feel that some knowledge of measure theory (which I lack...) would vastly help in grasping some of the more advanced chapters.

If you want to understand the Probability Theory, this book is the best instrument that I know for this target. It deals with probabilities without using complex numbers: the characteristic function doesn`t appear in it: the autor used the moment generating function instead. In this book you can find the most important things about the normal distribution and other continuous distribution related with it: the Student t-distribution, the chi-square and the Snedecor's F-distribution, the Cauchy distribution... For every important result you may find a lot of examples where you can apply it or counterexamples which shows you where you can't. It's a strong book to know the estimation of a real parameter or to learn how to test statistical hypotheses. It handles too with nonparametric inference. You can find also random vectors in this book, but you may find better books if you're interesting in this matter.

I studied with this book at the university. The first 4 chapters are a perfect (in a didactic viewpoint) introduction to (a formal and rigorous) probability theory.
Not so good in teaching random vectors (R², R³ etc.), has two good chapters for inference (a really good introduction, but not a full presentation).
OBS: forget Bayesian and nonparametric methods. The chapters dedicated to this subjects are not that good.