Graphical Models: Methods for Data Analysis and Mining Review

Average Reviews:

(More customer reviews)The book gives a good, very deep introduction to the topic of Graphical models and data mining. The main focus is on the data mining section, thus the reader should have a basic knowledge about the graphical model concept. It is certainly not a beginner's book or a tutorial on graphical models or Bayesian networks. Furthermore the book is very mathematical with quite a lot of definitions, lemmas and proofs. A good knowledge in set theory is mandatory. However, the theory is very well explained and illustrated with simple examples.
At some points I would have been more interested in more practical issues, however this may be an engineers view. From my point of view, the main drawback of the book is the strong focus on possibility theory.
However, I highly recommend this book for everybody interested in Graphical Models and especially in reasoning with possibility theory instead of probability theory. The reader should bring a good mathematical background. Then the book does not only provide good examples, but a knowledge based on a strong mathematical formalism. This allows the reader to fully understand the topic. Reading this book takes time and a lot of effort, but you can certainly benefit more from it than from most other books about this topic.

Click Here to see more reviews about: Graphical Models: Methods for Data Analysis and Mining

The concept of modelling using graph theory has its origin in several scientific areas, notably statistics, physics, genetics, and engineering. The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and is the first to include detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data. One major advantage of graphical modelling is that specialised techniques that have been developed in one field can be transferred into others easily. The methods described here are applied in a number of industries, including a recent quality testing programme at a major car manufacturer.* Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data* Each concept is carefully explained and illustrated by examples* Contains all necessary background, including modeling under uncertainty, decomposition of distributions, and graphical representation of decompositions* Features applications of learning graphical models from data, and problems for further research* Includes a comprehensive bibliographyAn essential reference for graduate students of graphical modelling, applied statistics, computer science and engineering, as well as researchers and practitioners who use graphical models in their work.

Click here for more information about Graphical Models: Methods for Data Analysis and Mining

Read More...

Learning in Graphical Models (NATO Science Series D: (closed)) Review

Average Reviews:

(More customer reviews)The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.
To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.
What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

Click Here to see more reviews about: Learning in Graphical Models (NATO Science Series D: (closed))

In the past decade, a number of different research communitieswithin the computational sciences have studied learning in networks,starting from a number of different points of view. There has beensubstantial progress in these different communities and surprisingconvergence has developed between the formalisms. The awareness ofthis convergence and the growing interest of researchers inunderstanding the essential unity of the subject underlies the currentvolume. Two research communities which have used graphical or networkformalisms to particular advantage are the belief networkcommunity and the neural network community. Belief networksarose within computer science and statistics and were developed withan emphasis on prior knowledge and exact probabilistic calculations.Neural networks arose within electrical engineering, physics andneuroscience and have emphasised pattern recognition and systemsmodelling problems. This volume draws together researchers from thesetwo communities and presents both kinds of networks as instances of ageneral unified graphical formalism. The book focuses on probabilisticmethods for learning and inference in graphical models, algorithmanalysis and design, theory and applications. Exact methods, samplingmethods and variational methods are discussed in detail. Audience: A wide cross-section of computationally orientedresearchers, including computer scientists, statisticians, electricalengineers, physicists and neuroscientists.

Click here for more information about Learning in Graphical Models (NATO Science Series D: (closed))

Read More...

Introduction to Graphical Modelling Review

Average Reviews:

(More customer reviews)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, Rubin 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.


Click Here to see more reviews about: Introduction to Graphical Modelling

A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.

Get 20% OFF

Click here for more information about Introduction to Graphical Modelling

Read More...

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Review

Average Reviews:

(More customer reviews)Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.

Click Here to see more reviews about: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

A general framework for constructing and using probabilistic models ofcomplex systems that would enable a computer to use available information for makingdecisions.

Get 25% OFF

Click here for more information about Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Read More...

Petri Net Theory and the Modeling of Systems Review

Average Reviews:

(More customer reviews)Thank you for your book, James! It's invaluble source of information necessary to start. It's like "The C Programming Language" by Brian Kernigan, "The art of computer programming" by Donald Knuth. Undoubtly this book is monument in the Petri Nets area. Everyone, who wants to work in the asynchronous systems modelling field, MUST READ this book FROM COVER TO COVER.

Click Here to see more reviews about: Petri Net Theory and the Modeling of Systems

Click here for more information about Petri Net Theory and the Modeling of Systems

Read More...

Modeling and Reasoning with Bayesian Networks Review

Average Reviews:

(More customer reviews)While pursuing my PhD at UCLA, I took Professor Darwiche's classes and had the privilege of using the pre-release version of this book. Before taking professor Darwiche's class, I had spent a good deal of time while working on my masters degree working on Bayesian networks. I found that much of the literature on Bayesian networks was inaccessible to someone new to the field. There simply was not a comprehensive resource that would explain Bayesian networks from the beginning in a through and clear manner. I say with confidence that this has now changed.
The book begins with the fundamentals of logic. It continues on to describe the properties of the Bayesian network graph such as independence relationships and d-separation as well as how the parameters of a Bayesian network work.
There are then in depth discussions of the various queries we are able to perform on Bayesian networks and the algorithms for accomplishing them. These include queries such as probability of evidence, most probable explanation and probabilistic inference. Techniques such as summing out, pearl's polytree algorithm and belief propagation are described elogently and clearly.
The book also contains information on the current state of the art research going on in the field. This book is a valuable resource for anyone new to or ingrained in the use of Bayesian Networks. A book of this scope and target was sorely needed and I for one am glad it has arrived. I would and have recommended this to any of my peers in the field.

Click Here to see more reviews about: Modeling and Reasoning with Bayesian Networks

Get 20% OFF

Click here for more information about Modeling and Reasoning with Bayesian Networks

Read More...