Modeling

The authors give a nice overview of computer performance evaluation using queueing theory and continuous and discrete-time Markov chains. After a short review of the relevant probability and statistics, the authors discuss Markov chains in the second chapter, pointing out that Markov processes can be used to model queueing systems even when these systems have behavior governed by non-exponential distributions. They characterize these as Markovizing methods. Their treatment of both discrete and continuous time Markov models is short but adequate, covering all the necessary concepts such as ergodicity and irreducibility. They then give a thorough discussion of the modeling process as actually done in practice. Their discussion of model sizing sets up their methodologies for dealing with large models later in the book. Performance measures for system requirements are discussed in terms of Markov reward models. Their treatment here is very detailed and they also give a large collection of helpful references on the subject.Petri nets are also discussed in the context of model generation. The authors state, correctly I think, that more time should be spent of developing models rather than the underlying mathematics. In their treatment of networks with non-exponential service time and interarrival time distributions, the authors employ the diffusion approximation via the solution of the Fokker-Planck equation. The don't discuss this in detail but give references for those who can read German. This would have been a place for a detailed analysis and derivation, given the surprising introduction of the Fokker-Planck equation in queueing theory. They also use, interestingly, maximum entropy methods to get approximate solutions of open and closed queueing networks. A very short chapter on optimization is given in the next chapter, which could stand to be more lengthy given the importance of this in implementing networks commercially. The next chapter covers some of the performance tools that are available for studying networks. The Performance Evaluation and Prediction System (PEPSY), stochastic Petri net package (SPNP), the CSPL language, the Model Description Language (MOSEL), the symbolic hierarchical automated reliability performance evaluator (SHARPE) are discussed with examples of each. Readers not having these tools will of course will not benefit too much from reading this chapter, except for maybe to get an idea of what is available. The OPNET and Ns-simulator packages,which are very nice modeling tools are not treated at all for some reason.

The last chapter covers applications, with case studies of queueing networks, Markov chains, stochastic Petri nets, and hierarchical models. Although of somewhat limited value in practice, the examples given do give the reader an idea of how the material in the book can be applied. And here again, the authors stress the use of modeling packages such as SHARPE and PEPSY, to verify the calculations in the case studies. They consider a closed non-product form queueing model of a medium-sized LAN in some detail with Ethernet links and a FDDI ring, solving it using Marie's method. Also interesting is their model of the UNIX operating system, which is also represented by a closed non-product queueing network. They compare the computation time needed to solve the model using CTMC, shadow, and DES techniques. Although the discussion is rather hurried, their model of an ATM network is also interesting, in that they use Markov reward models, obtaining both the state and transient solutions.

The book is one that will be of great assistance to those doing network modeling, performance analysis, and other time-scheduling modeling activiites. It is somewhat expensive, but worth the price I think considering the care which the authors take in their exposition.

Queueing Networks and Markov Chains is a scholarly book written by internationally recognized experts in their field. The book provides both basic concepts and complex topics. The book is embellished with multiple illustrations, solved examples, and problems. In my opinion this book should be useful to researchers and practitioners of the queueing theory discipline as a convenient reference, and to students as a useful textbook. Look at the price! It's worth it.

An excellent guide to better understand what's going on with the simulator modelling our unit. Specially good for the CDU, very applicable advices giving more trust in results.

It's one of the best technical books I have ever bought. It's very easy to read, gives very good advice and covers the whole subjet. I also like that the author does not try to sell any process simulator. I hate it when an author tries to sell products with his/her books. This one is very good, because you're helped regardless of the process simulator you prefer.

I recommend buying this book very much.

This book taught me a lot about blind children. The basic plot was interesting, but was not developed very much. The book focused more on the relationships of the characters in the book than on the mystery. Overall, I would recommend it to readers who do not get impatient if there is not a lot of action. Otherwise, people might find this book boring.

I am 46 yrs old and I read Secret of the Emerald Star in the fourth or fifth grade. It left an everlasting sensation on a certain summer day, when the air seems just right that brings me back to the day I read that book for the first time. It was long ago given away in a box of paperback books purchased from the scholastic book club or something like that. I have recurring flashbacks of the scenes in the book and how I hoped to befriend a blind girl to see if she was the same as the character in the book. I perceived these thoughts at 10 or 11 years old and have been on a mission since this past summer to find the old friend and see how I like it 35 yrs later...I will send the REAL review at a later date to see if I am pleasantly pleased or disappointed this time around.

Good basic introduction in semantic modelling. Gives a basic overview of limitation in other approaches (Relation, ER, OO) Works for database experts as well as novices. Section with modelling concepts good be more explored and descriptive.

I've used semantic data modeling as independent consultant and employee over the years, and I love it. The Xplain data definition and manipulation language is just great to create consistent data models. This book is an excellent introduction to that language. In my experience Xplain diagrams are far easier to understand that ER diagrams. Handy if you come in new to a project and need to understand the database: just draw an Xplain diagram. With an Xplain diagram most of the time I can also immediately offer suggestions for improvement.

Another advantage is that semantic modeling recognizes only inheritance and aggregation, so it fits well in an OO approach to software development.

So if you do a lot of data modeling and data base design I suggest you get hold of a copy. Your data models will improve, guaranteed!

The book is quite good for experienced spice users. I have used the software off and on for years, but found the information in the book to be an eye opener with regard to new approaches to using the product. The other Kielkowski book is more appropriate for beginning users.

I have been a SPICE user for a few years, but have never really known much about it beyond what it does. I needed to model a new device, and started looking for a program to do so. After i discovered the extreamly high cost, i decided to buy this book. The book is well laid out, with a nice focus on practical work. It doesn't talk about the general's of SPICE, so you may want to try his other book as well. However I found the book to be extreamly useful, and the book is well-written so I found that I didn't need another (intro to SPICE) book to figure out the syntax of SPICE. There are several examples you can study, so probably you could figure it out from them.
The only problem is that sometimes the author refers you to using expensive equipment to measure a real devics parameters. However you do not need this, and the book shows you how to get the required information off of a datasheet. The book's pretty expensive, but nice. Make sure you check out the sample chapters to decide if the books for you!

The author's step-by-step explanations are great, and the issue coverage superb. The CD-Rom was a disappointment, however, as it contains simply the book text and graphics; I expected sample, working spreadsheets like that found in Benninga's Financial Modeling book. Despite the shortcoming, Holden's book is worth the money.

This is NOT a handy book of spreadsheet templates for use in finance. It IS a book to TEACH you about building spreadsheets for use in finance. The author has built dozens of very useful spreadsheets that he gives you step-by-step instructions on building and WHAT the step is about.

If you build all of the spreadsheets in the book you will gain a great deal of understanding about the subjects covered in the book and will be miles ahead of the calculator-based approach typical in today's classrooms. No professionals use calculators to figure duration or convexity or optimal portfolios, why should you? This is a very needed book and a nice approach to the subject.

I like this version of the book MUCH better than the Fundamentals version. But that is my preference; pick the book that is right for you. They are both very good. I intend to get more in the series.

the book looks pretty good but i was wondering what heppened with the CD that comes with the book, if anyone knows please let me know

This workbook is truly the strongest complement to the classroom. It allows the student to flesh out the theoretical concept while building an important skill set for anyone who wants a career in finance. A wise investment...looking forward to the new titles coming out in the fall!

This book is a detailed overview of the computational modeling of nervous systems from the molecular and cellular level and from the standpoint of human psychophysics and psychology. They divide their conception of modeling into descriptive, mechanistic, and interpretive models. My sole interest was in Part 3, which covers the mathematical modeling of adaptation and learning, so my review will be confined to these chapters. The virtue of this book, and others like it, is the insistence on empirical validation of the models, and not their justification by "thought experiments" and arm-chair reasoning, as is typically done in philosophy.

Part 3 begins with a discussion of synaptic plasticity and to what degree it explains learning and memory. The goal here is to develop mathematical models to understand how experience and training modify the neuronal synapses and how these changes effect the neuronal patterns and the eventual behavior. The Hebb model of neuronal firing is ubiquitous in this area of research, and the authors discuss it as a rule that synapses change in proportion to the correlation of the activities of pre- and postsynaptic neurons. Experimental data is immediately given that illustrates long-term potentiation (LTP) and long-term depression (LTD). The authors concentrate mostly on models based on unsupervised learning in this chapter. The rules for synaptic modification are given as differential equations and describe the rate of change of the synaptic weights with respect to the pre- and postsynaptic activity. The covariance and BCM rules are discussed, the first separately requiring postsynaptic and presynaptic activity, the second requiring both simultaneously. The authors consider ocular dominance in the context of unsupervised learning and study the effect of plasticity on multiple neurons. The last section of the chapter covers supervised learning, in which a set of inputs and the desired outputs are imposed during training.

In the next chapter, the authors consider the area of reinforcement learning, beginning with a discussion of the mathematical models for classical conditioning, and introducing the temporal difference learning algorithm. The authors discuss the Rescorla-Wagner rule , which is a trial-by-trial learning rule for the weight adjustments, in terms of the reward, the prediction, and the learning rate. They then discuss more realistic policies such as static action choice, where the reward/punishment immediately follows the action taken, and sequential action choice, where rewards may be delayed. The authors discuss foraging behavior of bees as an example of static action choice, reducing it to a stochastic two-armed bandit problem. The maze task for rats is discussed as an example of sequential action choice, and the authors reduce it to the "actor-critic algorithm." A generalized reinforcement learning algorithm is then discussed, with the rat water maze problem given as an example.

Chapter 10 is an overview of what the authors call "representational learning", which, as they explain, is a study of neural representations from a computational point of view. The goal is to begin with sensory input and find out how representations are generated on the basis of these inputs. That such representations are necessary is based on for example the consideration of the visual system, since, argue the authors, what is presented at the retina is too crude for an accurate representation of the visual world. The main strategy in the chapter is to begin with a deterministic or probabilistic input and construct a recognition algorithm that gives an estimate of the input. The algorithms constructed are all based on unsupervised learning, and hence the existence and nature of the causes must be computed using heuristics and the statistics of the input data. These two requirements are met via the construction of first a generative model and then a recognition model in the chapter. The familiar 'expectation maximization' is discussed as a method of optimization between real and synthetic data in generative models. A detailed overview of expectation maximization is given in the context of 'density estimation'. The authors then move on to discuss causal models for density estimation, such as Gaussian mixtures, the K-means algorithm, factor analysis, and principal components analysis. They then discuss sparse coding, as a technique to deal with the fact that the cortical activity is not Gaussian. They illustrate an experimental sample, showing the activity follows an exponential distribution in a neuron in the inferotemporal area of the macaque brain. The reader will recognize 'sparse' probability distributions as being 'heavy-tailed', i.e. having values close to zero usually, but ones far from zero sometimes. The authors emphasize the difficulties in the computation of the recognition distribution explicitly. The Olshausen/Field model is used to give a deterministic approximate recognition model for this purpose. The authors then give a fairly detailed overview of a two-layer, nonlinear 'Helmholtz machine' with binary inputs. They illustrate how to obtain the expectation maximization in terms of the Kullback-Leibler divergence. The learning in this model takes place via stochastic sampling and occurs in two phases, the so-called "wake and sleep" algorithm. The last section of the chapter gives a general discussion of how recent interest in coding, transmitting, and decoding images has led to much more research into representational learning algorithms. They discuss multi-resolution decomposition and its relationship to the coding algorithms available.

This book is certainly the most thorough textbook currently available
on many aspects of computational neuroscience. It works very carefully
through the fundamental assumptions and equations underlying large
tracts of contemporary quantitative analysis in neuroscience. It is
an ideal introductory book for those with a quantitative background,
and is destined to become a standard course book in the field.

A lot of companies including Apress tried to saturate the market with .Net books while Visual Studio.Net was in the Beta. This book is an example of that. There are 2 editions for this book, one is based on the Beta 2 and the other on the initial release of Visual Studio.Net (1.0). The Beta book has several noticeable errors and the editing was a rush job. Some of the examples resemble the on-line help and may be screen shots from the on-line help. The on-line help has improved tremendously since the Beta. The 2nd edition has corrected most of those errors and the editor has done a higher quality job. The chapters on disconnected databases (Chapters 9 thru 12) and XML (Chapter 22) are very good for understanding some of the new features of ADO.Net. Microsoft made several changes to ADO.Net and Visual Basic.Net while these products were Beta, so you want to buy the 2nd edition only. The Beta book may mislead you on several key issues.

Very detailed book and extremely neat and tidy writing. Not so much more to add other than this is a great way of getting to know ADO.net and otehr database technologies and techniques.

Although this is a somewhat old book, ie. it covers 1.0 of the framework its still an excellent book about many types of data access with .net, especially ado.net. I can recommend this book to anyone wanting to learn ado.net

This book is a terrible guide to UML. The authors constantly give their opinions on components of UML and fail to define or illustrate the components clearly or accurately. Irrelevant metaphors and "jibber-jabber" are constantly used throughout every chapter. The further the book goes into UML and the more complex the subject becomes - the more vague and misleading the book becomes. I would not recommend this book to anyone attempting to learn or use UML. Whether this book is assuming that the reader knows UML or not, it would be incomplete for both types of readers.

If you start your study of the Unified Modeling Language(UML) by going to the definitive references by the three creators, it is quite likely that you will be intimidated. The three books, _The Unified Modeling Language User Guide_, _The Unified Modeling Language Reference Manual_ and _The Unified Software Development Process_, all written by the designers and published by Addison-Wesley, are nearly 1500 pages of rather intense material. Like a veteran horseman, Martin Fowler charges to the rescue.
In a "mere" 174 pages, he takes each of the essential main areas of the UML and presents a brief, yet surprisingly thorough description of what it is and how it is used. While targeted at the UML novice, it is necessary to have a fairly solid background in object-oriented programming in order to understand it. Since the UML is a modeling language based heavily on diagrams, they are used throughout the book and are very effective.
This book will not teach you the UML, that task is left to weightier works. However, it will provide the proper foundation so that you can learn it, a task that is just as important. I listed the first edition as one of the best books of the year in my "On Books" column that appeared in the September, 1998 issue of _Journal of Object-Oriented Programming_ . There is nothing in the second edition that will change that opinion.

More books should be like "UML Distilled:" concise and readable. Martin Fowler and Kendall Scott select the parts of UML that you need, and present them in an easy to read style. More valuable than a mere description of the modeling language, however, is the author's insight and experience in how to use this technique to communicate and document design.

Quite simply, UML is the technique that developers use to communicate their object-oriented software design and planning. A picture is worth a thousand words, and UML makes sure that everybody can read your paintings. If you're not familiar with object-oriented programming, then you may want to start here.

Chapters 1 and 2 give a brief history of UML, and a quick overview of a generic software process, touching on techniques such as agile methodologies, refactoring, patterns, and test. Chapters 3 through 5 get started on UML with use cases, class diagrams, and interaction diagrams. Throughout, Fowler gives details on how and when he uses these -- and more importantly, when he doesn't.

Despite the conversational tone, this book is designed as a reference. The authors go so far as to tell you to skip chapter six until you need it. Such helpful pointers are a godsend to mere mortals trying to accomplish something. This chapter focuses on more advanced elements within object diagrams, probably the most heavily used diagram type.

Chapter 7 focuses on packages, which are used to describe high level relationships within the system. Again, Fowler's advice is welcome on how to best use these diagrams in the real world. Chapter 8 examines the state diagrams, providing a standard way to use and document these designs. Chapter 9 centers on activity diagrams, which standardize and update traditional flow charts. He touches on deployment and component diagrams, but wisely leaves the details to others.

There are lots of books on UML. Unfortunately, most are unapproachable due to their didactic tone, detail, and sheer volume. "UML Distilled," on the other hand, focuses on key knowledge, conversational tone, and brevity to emphasize the day to day use of UML. Many people simply will never need another book on UML.