Customer Reviews:
An excellent overview.......2004-05-17
In just a decade, Bayesian networks have went from being a mere academic curiosity to a highly useful field with myriads of applications. Indeed, the applications of Bayesian networks are wide-ranging and include disparate fields such as network engineering, bioinformatics, medical diagnostics, and intelligent troubleshooting. This book gives a fine overview of the subject, and after reading it one will have an in-depth understanding of both the underlying foundations and the algorithms involved in using Bayesian networks. The reader will have to look elsewhere for applications of Bayesian networks, since they are only discussed briefly in the book. Due to space constraints, only the first four chapters will be reviewed here.
The author defines a Bayesian network as a graphical structure for representing the probabilistic relationship among a large number of variables and for performing probabilistic inference with these variables. Before the advent of Bayesian networks, probabilistic inference depended on the use of Bayes' theorem, which entailed that the problems examined be relatively simple, due to the exponential space and time complexity that can arise in the application of this theorem.
After a short review of probability theory in chapter 1, a discussion of the "philosophical" foundations of probability, and a discussion of the difficulties inherent in representing large instances and in performing inference over a large number of variables, the author introduces Bayesian networks as directed acyclic graphs satisfying the Markov condition. A brief discussion of NasoNet, which is a large-scale Bayesian network used in the diagnosis and prognosis of nasopharyngeal cancer, is given. The author then shows in detail how to create Bayesian networks using causal edges, introducing in the process the notion of manipulating variables and the notion of a causation between two variables. An interesting example of manipulation is given in the context of pharmaceuticals, and an example of bad manipulation is given.
Chapter 2 addresses the nature of dependencies in DAGs via the concept of `faithfulness' and entailed conditional independencies. Very important in this chapter is the notion of `d-separation', which identifies all and only those conditional independencies entailed by the Markov condition for G. An explicit algorithm is given for finding d-separations. D-separation is used to define a notion of Markov equivalence between DAGs containing the same set of nodes. Also discussed is the minimality condition, wherein a DAG will not satisfy the Markov condition with respect to a probability distribution if an edge is removed from it. The author shows every probability distribution satisfies the minimality condition with some DAG. The notion of a `Markov blanket' is introduced, which measures the extent to which the instantiation of a set of nodes close to a particular node can shield the node from the effect of all other nodes. A Markov boundary of a random variable is then defined as a Markov blanket such that none of its proper subsets is a Markov blanket of the random variable. The utility of these concepts lies in the fact that the set of all parents of each variable X, children of X, and parents of children of X are the unique Markov boundary of X, if the DAG satisfies the faithfulness condition.
Inference in Bayesian networks is the topic of chapter 3, with Pearl's message-passing algorithm starting off the discussion for the case of discrete random variables. This algorithm, which applies for Bayesian networks whose DAGs are trees, is based on a theorem, whose statement takes well over a page, and whose proof covers five pages. The author gives detailed examples though, and these are very helpful in understanding the algorithm. The Pearl algorithm is then generalized to singly and multiply connected networks. After a discussion of the computational complexity of the algorithm, the author then overviews the `noisy OR-gate model', which is a model whose complexity is manageable, since each variable in the model has only two values. The author then moves on to doing inference using an approach, called `symbolic probabilistic inference' that approximates finding the optimal way to compute marginal distributions of interest from the joint probability distribution. This algorithm involves a number of multiplications in order to compute the marginal probability. To minimize the computational effort, it would be advantageous to minimize the number of these multiplications, and so the author discusses the `optimal factoring problem', which, once solved for a given factoring instance, will give a factorization that requires a minimal number of multiplications. What follows after this is a very interesting discussion of the relationship of human reasoning to Bayesian networks. This is done via the introduction of the `causal network model', and the author then, quite unexpectedly, overviews the research on the testing of human subjects so as to test the accuracy of the model. These testing studies included those that involve inference based on `discounting', which measures to what degree an individual becomes less confident in the cause when told that a different cause of the effect was present. Another discussed is one that involves larger networks in the context of traffic congestion. This is followed by a discussion of a study of causal reasoning in the context of the debugging of programs.
Inference algorithms are studied for the case of continuous variables in chapter four. After a review of the normal probability distribution, the author discusses an inference algorithm for the case of Gaussian Bayesian networks. An algorithm for doing inference with continuous variables for singly connected Bayesian networks is given, that allows the determination of expected value and variance of each node conditioned on specified values of nodes in some subset. This is followed by several detailed and helpful examples of inference in continuous variables. As expected, issues with computational complexity arise, and so the author discusses approximate inference, via the method of stochastic simulation, which involves a classical sampling method called `logic sampling.' This is then followed by a discussion of likelihood weighting, which cures some of the problems involved with logic sampling. Abductive inference, so important in contemporary applications, is then discussed in detail.
Enjoying this book enormously.......2004-01-04
Rarely do I find myself reading a technical book
so carefully as this one. I always enjoy
books on Bayesian inference,
but this is the first that shows me how
to write useful algorithms. I appreciate
the level of mathematical rigor, too, for
such a new subject. Bayesian networks are what
neural networks should be, without the ad-hoc
theory and trial-and-error algorithms.
Average customer rating:
- Outstanding
- Great Start to Machine Learning
- Best book I've seen on topic
- too expensive I would say
- Excellent book, concise and readable
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Machine Learning
Tom M. Mitchell
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Artificial Intelligence: A Modern Approach (2nd Edition)
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Introduction to Machine Learning (Adaptive Computation and Machine Learning)
ASIN: 0070428077 |
Book Description
This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
Customer Reviews:
Outstanding.......2007-09-12
I read this book about 7 years ago while in the PhD program at Stanford University. I consider this book not only the best Machine Learning book, but one of the best books in all of Computer Science. It covers every branch of ML I know of and it covers it really well. I found Mitchell's chapter on Neural Networks more insightful than an entire book on NN's that I read. I also found his chapter on Reinforcement Learning more useful and better explained than an entire book on Reinforcement Learning that I also read. The other chapters cover other ML topics at the same level of quality and rigor.
The author did an amazing job in covering the breadth and depth of ML in less than 500 pages. I hope he will write a new edition to cover the advances that happened in the last decade.
Great Start to Machine Learning.......2007-08-27
I have used this book during my masters and found it to be an extremely helpful and a gentle introduction to the thick and things of machine learning applications. The various chapters are nicely paced with helpful problems at the end. Another great thing about the book is treatment of detailed examples with each concept and that the author carefully ties various concepts as they arise, with not just new, but also examples from previous chapters, which helps the user to understand different concepts applied to same problems thereby making clear difference between different methods. Also the author has a dedicated website with updated errata and notes, which is also very helpful! Having said that, I think the book is an introduction to various machine learning methods and one can easily follow on the references listed for detailed treatment of relevant topics.
Best book I've seen on topic.......2007-01-31
I have this book listed as one of the best and most interesting I've ever read. I loved the book just as much as I loved the course we used it in.
I have a genuine interest in AI and especially Machine Learning and this book both inspired me as well as clared some things up for me. Like how the spectrum of different known methods differ in their appoach of different problems.
This book is also very concise and well written, without being too mathematical. Making it very easy to read and understand.
Ever since I took that course I keep returning to this book as a reference when I have a related problem to solve, or just bothering my mind.
Highly recommended!
too expensive I would say.......2006-10-13
great book if you wanna start sth anywhere in machine learning, but it is toooooo expensive.
Excellent book, concise and readable.......2006-06-22
This is a great book if you're starting out with machine learning. It's rare to come across a book like this that is very well written and has technical depth. The writing is to the point, maybe even a bit terse, but all that you need to know is in there. It's a bit old so doesn't cover kernel methods or SVM's but is still a great first machine learning book.
Book Description
With Bayesian network technology very much on the up-swing in industry and government, there is an increasing need for an introductory book that jointly emphasizes the understanding of its underlying priniciples and their application in practice. Bayesian Artificial Intelligence presents elements of Bayesian network technology, automated causal discovery, and learning probabilities from data along with extensive motivational examples of using these technologies to develop probabilistic expert systems. This practical, very accessible introduction balances the causal discovery of networks with the Bayesian inference procedures that use a network once it is found. The authors emphasize understanding and intuition, so they keep the mathematical details to a minimum, but also provide the algorithms and technical background needed for applications. They illustrate at length a number of applications and discuss application software in detail. A broad range of topics, a practical perspective, and a thoughtful discussion of philosophical underpinnings make Bayesian Artificial Intelligence an ideal introduction for students and for professionals who want to broaden their expertise. It provides the knowledge you need to put Bayesian network tools into practice, while also forming the basis for a more detailed investigation of the technology and original research.
Customer Reviews:
Very good introduction in causal Modeling.......2006-03-09
The book by Korb and Nicholson is very readable and structured. Starting with some background information in statistics it comes directly to the major topic of the book - bayesian networks. The theory thereof is nicely evolved and applied to small examples to demonstrate its usage. Each chapter finishes with a short summary and bibliographical notes for further readings.
In my opinion this book is well written and the chosen examples are insightful. What I do not like is part three of the book which is devoted to case studies and praktical examples. If this space had been used for the first two parts by providing more details, e.g., for the discussion of path models (which is given but only short), this book could be even great on a more advanced level. In this form it is very good as an introduction in Bayesian Networks and related topics like the larger class of causal models.
Excellent Introductory Text.......2004-12-17
It is difficult to assess a review without understanding the biases of the reviewer. I fall under the category of researcher/practitioner when it comes to reasoning with graphical models. I am familiar with and make use of several books and papers on this topic in my work. Of the set of standard references (Pearl, Jensen, Neapolitan, Jordan, Cowell et al., Borgelt & Kruse) the text by Korb and Nicholson (K&N) stands out in terms of its clarity and accessibility. Does the book have everything one would ever want to know about Bayesian inference? Not by a long shot. Is it, however, a good place to start? Definitely. The basic concepts are presented relatively completely and with clarity. I consistently recommend K&N over other alternatives to colleagues new to the field. Is there a chasm separating concept and algorithm in the book? I don't think there is, especially relative to other references. With tools such as Kevin Murphy's BNT, or Netica available on the Web, it seems to me that providing a solid conceptual framework becomes paramount for a textbook such as this. I believe K&N succeed admirably in this sense. Why four stars and not five? Even for an introductory text such as K&N, it would be nice to have more development of some concepts such as causality, context specific independence, or loss of independence in dynamic nets. Although it won't be your last book on reasoning with graphical models, K&N should probably be your first.
Bayesian Networks for Undergrads and Practicioners.......2004-01-12
Despite its name "Bayesian Artificial Intelligence" covers Bayesian network (BN) techniques only. Other Bayesian techniques useful for AI are not treated.
The content is divided in three main sections: (1) The basics of probabilistic reasoning with BNs, (2) Causal discovery (finding BNs from data), and (3) "Knowledge engineering".
The first part covers the fundamental concepts and algorithms around BNs and (simple) decision networks. It is well-written and clear, but readers who are not totally new to the field might find only little new information (e.g., loopy belief propagation, continuous densities, large decision networks, etc. are not covered).
The second part is on how to deduce causal relationships from observational data. Constrained-based and Bayesian approaches are covered, but on a rather general level. I am not sure how easy it is to implement the algorithms from the descriptions provided. When it comes to details of the algorithms, proofs, or mathematical background the authors very often refer to the literature due to "lack of space". From a practical standpoint, it is unfortunate that the different methods are compared to each other only superfiscially. For instance, one method presented performs a large number of statistical tests; one would expect that this requires large amounts of data in order to avoid false positive results. Is this a problem? With questions like these the reader is often left alone.
I am not competent to talk about part three (knowledge engineering), so I end with my general impression of the book: I would have appreciated if the authors had treated some the algorithms in greater detail and had spent a few pages on advanced concepts and current research directions. On the other hand, some information provided could have easily been left out. (For instance, how to download and install certain software packages from the internet, Kevin Murphy's well-known survey on BN software packages, screenshots of user dialogs, etc. just eat pages. Providing the URLs to the corresponding sites on the internet is completely sufficient, and the information there is more likely to be up-to-date.) The saved pages could then be spent on information which is not readily available elsewhere.
To summarize: The book provides a mostly well-written general overview of the basic concepts and could serve as a first introduction to the field. However, it leaves the reader often alone when it comes to the mathematical background, potential practical pittfalls, or advanced algorithms.
Book Description
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.
The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models.
The book is a new edition of
Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also
- provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes.
- give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge.
- give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs.
- present a thorough introduction to state-of-the-art solution and analysis algorithms.
The book is intended as a textbook, but it can also be used for self-study and as a reference book.
Customer Reviews:
Good Book.......2006-03-01
For an introduction to the subject, this book is unequivocal in my experience with the literature. Great read that has propelled me forward into combining a bayesian network with a physical model to approach a very complex sediment transport problem.
A very good introduction to Bayesian networks.......2003-06-15
I am very pleased to have found a book that gives a modern, sound, and self-contained introduction to Bayesian networks. The only prerequisite is basic knowledge of probability. This makes sense because a Bayesian network is essentially a directed graph whose vertex set is a collection of random variables, while an edge from one variable X to another variable Y represents a belief that X has a causative effect on Y. For example, X could be the pregnancy status of a cow, while Y could be a blood test administered to the cow. Vertex Y would contain a contingency table that reflects the conditional probability of Y in terms of X. The author does well in explaining this, as well as adequately treating many of the practical issues surrounding Bayesian networks, such as design issues, network learing and tuning, and some basic algorithms (e.g. bucket elimination and junction trees) that aid in the efficient updating of variable probabilities due to new evidence that may instantiate or change the distribution of one or more variables.
The author also provides a good introduction to decision graphs, a close relative of Bayesian networks.
The aspect of Bayesian networks that I find most attractive is the fact that there is a "rational" way of designing a network, based on hypothesis, informational, and mediating variables, and their "causal" relationships. Unlike neural networks in which one is almost forced to guess the appropriate structure of the network, every node in a Bayesian network correpsonds with a state or quantity that can be measured either directly or indirectly through other variables. Thus, changes in a system model should only induce local changes in a Bayesian network, where as system changes might require the design and training of an entirely new neural network.
Another aspect of Bayesian networks that I find very compelling is the way in which they seem quite amendable to learning and the presentation of new evidence. This is true since knowledge updating is done locally (through variables), while the effects of those changes are witnessed globally through appropriate belief-updating algorithms.
On the downside, it should be noted that the operation of belief-updating is in general NP-hard, thus there exists a valid concern about the computational efficiency of Bayesian networks. Contrast this with the fact that once a nueral network has been trained, it is quite easy to compute. One would hope that these concerns will subside with more research, for the above mentioned benefits of Bayesian networks leads me to believe that these networks will have quite an influence on the future directions of machine learning.
Although this book will not go down in history as the definitive reference for Bayesian networks, it serves as a good conduit for explaining this quite interesting area of learning at a time when such few complete and modern references exist.
A lot about very little.......2003-05-06
The book covers many topics, but doesn't really cover them well. I would not recommend this book. I have learned litte from it.
Accessible introduction to Bayesian Networks.......2003-01-21
Among currently available introduction to Bayesian networks (also known as Bayes Net, Bayesian Belief Nets), this book is probably one of the most accessible. The book is divided into part I and II. Part I is intended for BN users (practitioners) and Part II more towards BN developers and researchers, as it contains algorithmic introduction of BN.
Prerequisites of the book as stated in the preface include Graph Theory and Calculus, both at introductory level. I personally did not have exposure to Graph theory, but I was able to understand most of the material without any help. Necessary probability theory is developed, but basic probability knowledge is also a prerequisite to digest the material to a reader without prior exposure of Probability as it shapes the core of the material in the book.
The strength of this text is in Part I where the author provides several examples to illustrate use of Bayesian Networks, Influence Diagrams and other models. I find it useful Influence Diagram as an extension of Bayesian Networks.
Most answers to Exercises at the end of each chapter are provided at the author's homepage, except answers of the last chapter. Answers that require graphical modeling software are also provided in Hugin format. (Hugin Lite can be downloaded from Hugin site.)
The downsides are that writing of the text is somewhat awkward, obscuring readers from understanding, that model building chapter could have been discussed more thoroughly, that material in Learning is barely present, and that definitions are sometimes not introduced upon the first encounter but they appear later in chapters. More different and complex examples could have been discussed to illustrate the material. Note: the author provides a page for Learning at his homepage.
Although this is an introduction to Bayesian Networks and Influence Diagrams, a reader should be equipped with some level of abstract thinking in order to digest the material.
This book is suitable for self-study. It has motivations for the uninitiated. References are provided at the end of the book and I was able to find some of them online. A notable is "A tutorial on Learning with Bayesian Networks" by Heckerman, to fill in the part of Learning in this book.
Other books at this level from users' perspective are:
Edwards, Introduction to Graphical Modeling (Utilizes software MIM.)
Clemen, et al., Making Hard Decisions (Uses Palisade Decision Tools suite. The book discusses Influence Diagrams but not Bayesian Networks.)
Further studies after completion of this book include:
Cowell, et al., Probabilistic Networks and Expert Systems
Lauritzen, Graphical Models
Pearl, Probabilistic Reasoning in Intelligent Systems
Pearl, Causality
Not worth the money.......2002-12-31
Chapter 1 is a nice introduction to probability. Chapter 2 is readable. Chapter 3 is poorly presented, and you feel sad for having wasted so much money on a book with only one intelligible chapter.
Book Description
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.
This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.
Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Customer Reviews:
A great handbook for data mining.......2006-04-12
I'm surprisingly pleased with this book. The book is well-written and it is completely worth the price. The main chapters of the book are independent, so you can read them in any order. It nearly cover the entire data mining field. In fact you can find a good overview about almost all important data mining techniques. Moreover most of the algorithms are presented in pseudo code, so you can really learn how to implement them. I also liked the application section which describes real case studies - it gives you a good sense how to use these techinques.
Average customer rating:
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Advanced Methods for Knowledge Discovery from Complex Data (Advanced Information and Knowledge Processing)
Manufacturer: Springer
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ASIN: 1852339896 |
Book Description
This book brings together research articles by active practitioners and leading researchers reporting recent advances in the field of knowledge discovery. An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks. With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, this book will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field.
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Advances in Bayesian Networks (Studies in Fuzziness and Soft Computing)
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In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition,
Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
Nikolay Nikolaev , and
Hitoshi Iba
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This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks.
Adaptive Learning of Polynomial Networks is a vital reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and for advanced-level students of genetic programming. Readers will strengthen their skills in creating efficient model representations and learning operators that efficiently sample the search space, and in navigating the search process through the design of objective fitness functions.
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- Excellent book on neural networks and Bayesian methods
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Bayesian Learning for Neural Networks (Lecture Notes in Statistics)
Radford M. Neal
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Bayesian Data Analysis, Second Edition (Texts in Statistical Science)
ASIN: 0387947248 |
Book Description
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Customer Reviews:
Excellent book on neural networks and Bayesian methods.......1997-08-24
This book is a landmark in both neural networks
and in statistics. It describes a coherent and
powerful framework for using supervised neural networks, and it contains radical ways of making Bayesian Monte Carlo computations in high-dimensional spaces more efficient.
Average customer rating:
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Advanced Intelligent Computing Theories and Applications - With Aspects of Theoretical and Methodological Issues: Third International Conference on Intelligent ... Science) (Lecture Notes in Computer Science)
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback
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ASIN: 3540741704 |
Product Description
This book - in conjunction with the two volumes CICS 0002 and LNAI 4682 - constitutes the refereed proceedings of the Third International Conference on Intelligent Computing, ICIC 2007, held in Qingdao, China in August 2007.
The 139 revised full papers were carefully reviewed and selected from 2875 submissions. The papers are organized in topical sections on biological and quantum computing, intelligent financial engineering, intelligent agent and web applications, intelligent sensor networks, intelligent control and automation, intelligent data fusion and security, natural language processing and expert systems, intelligent image/document retrievals, intelligent computing in bioinformatics, intelligent computing in signal processing, intelligent computing in pattern recognition, and intelligent computing in communication.
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