Information Theory, Inference and Learning Algorithms Review

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(More customer reviews)Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.
This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way.In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

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Learning in Graphical Models (Adaptive Computation and Machine Learning) Review

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(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.

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Graphical models, a marriage between probability theory and graph theory,provide a natural tool for dealing with two problems that occur throughout appliedmathematics and engineering--uncertainty and complexity. In particular, they play anincreasingly important role in the design and analysis of machine learningalgorithms. Fundamental to the idea of a graphical model is the notion ofmodularity: a complex system is built by combining simpler parts. Probability theoryserves as the glue whereby the parts are combined, ensuring that the system as awhole is consistent and providing ways to interface models to data. Graph theoryprovides both an intuitively appealing interface by which humans can model highlyinteracting sets of variables and a data structure that lends itself naturally tothe design of efficient general-purpose algorithms.This book presents an in-depthexploration of issues related to learning within the graphical model formalism. Fourchapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks,David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on VariationalMethods, and David Heckerman on Learning with Bayesian Networks. The remainingchapters cover a wide range of topics of current research interest.

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Utility-Based Learning from Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition) Review

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(More customer reviews)This book is just as great inside the cover as
the elegant cover leads you to expect.
A very ambitious book with a very broad scope.
As a Professor of Applied Mathematics and
of mathematical finance, I very much look
forward to presenting parts of this material
in the future.
Concerning the contents, citing from the introduction of
the book:"Our point of view is motivated by the notion that probabilistic models are
usually not learned for their own sake-rather, they are used to make decisions"
and "finance and decision theory provide a language in which it is
natural to express these assumptions-namely, utility theory-and formulate,
from first principals, model performance measures and the notion of optimal
and robust model performance"
and the books purpose is : " to provide a pedagogical and self-contained discussion of a select set of
methods for estimating probability distributions that can be approached
coherently from a decision-theoretic point of view"
The last sentence is extremely telling. Friedman and Sandow indeed
demonstrate in this book that, in struggling to quantify
default risk, in their daytime jobs at Standard and Poor's,
they carefully put into place their own approach, and painstakingly
tested it on read data, throughout many different economic
cycles (as far back as 2001, when I worked in Friedman's group).
In addition, after Friedman presented some of this material at
New York University's Courant Institute, Friedman and Sandow saw fit to
include a through introduction to topics which are of interest
to all economic students, such as utility theory and
minimum relative theory. And they do so in a crisp, clear and no-nonsense
manner that is rarely seen in books on economics.
A key aspect of the point of view taken in this book, is to relate
betting odds, such as in a horse race, to expected
growth of wealth.
Readers should race to the bookstore to get a
hold of this book!

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Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used to make decisions. Specifically, the authors adopt the point of view of a decision maker who(i) operates in an uncertain environment where the consequences of possible outcomes are explicitly monetized,(ii) bases his decisions on a probabilistic model, and(iii) builds and assesses his models accordingly.These assumptions are naturally expressed in the language of utility theory, which is well known from finance and decision theory. By taking this point of view, the book sheds light on and generalizes some popular statistical learning approaches, connecting ideas from information theory, statistics, and finance. It strikes a balance between rigor and intuition, conveying the main ideas to as wide an audience as possible.

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Handbook of Mathematical Models in Computer Vision Review

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(More customer reviews)When attending a general computer vision conference like xCCV, did you ever feel lost at certain sessions? Well, don't always blame the presenters! The field covered by Computer Vision has become so broad that it is almost impossible to understand what is going on and to keep track of the latest developments. To (partially) overcome this problem, the editors of the Handbook of Mathematical Models in Computer Vision have done a great job.
One can become a bit skeptical reading such a title. How complete can such a handbook be? However, going through the 33 chapters, indeed a wide breadth is treated. The focus of the book is on mathematical methods that both model and reproduce human visual abilities. This is the field of biological vision in which the editors have a strong background.
The editors chose three distinct categories of mathematical models, namely variational techniques (those attending Prof. Faugeras' talk at ICPR 2006 may remember his statement that they give the fundamental equations in computer vision!), statistical methods, and combinatorial approaches. The chapters are grouped in six sections that circle around these three categories. Although going through the book chapters by mentioning keyword may yield a rather boring list, it shows the wide variety of topics that are being dealt with.
The book starts with a section on low-level vision: Image Reconstruction. Here one can find information on diffusion filters and wavelets, total variation methods, and PDE based inpainting.
The second section is concerned with Boundary Extraction, Segmentation and Grouping. Here subjects like levelings, graph cuts, minimal paths and fast marching methods, deformable models, variational segmentation with shape priors, curve propagation, level set methods, and a stochastic model of geometric snakes are discussed.
Section three switches to high level vision. It deals with Shape Modeling & Registration, divided into topics concerning invariant processing and occlusion resistant recognition, image-based inferences, point matching and uncertainty-driven, point-based image registration.
In the fourth section, Motion Analysis, Optical Flow & Tracking, the concept of time is added and one encounters the topics of optical flow estimation, image warping, alignment and stitching, visual tracking, image and video segmentation, human motion capture, and dynamic textures.
Section five deals with 3D from Images, Projective Geometry & Stereo Reconstruction, treated by boundary detection, stereo, texture and color, shape from shading, calibration, motion and shape recovery, multi-view reconstruction, binocular stereo with occlusions, and modeling non-rigid dynamic scenes.
The last section may seem a bit odd: Applications: Medical Image Analysis. However, this is one of the most prominent areas in computer vision. Although here certain vision aspects do not occur, compared to natural images (just think of the influence of the sun), for many tasks the performance of the mathematical methods can be evaluated since a ground truth is often available - provided by humans whom the models are supposed to mimic. In this section, applications of interactive graph-based segmentation methods, 3D active shape and appearance models, characterization of diffusion anisotropy, segmentation, variational approaches, and statistical methods of registration are given.
The danger of publishing an edited volume is the difference in style and treatment of the topics among the various contributions. This is not the case here. Each chapter gives a general introduction to the topic, introduces the mathematical model, discusses the underlying ideas globally, and shows some results. For the full details the readers are referred to the extensive bibliography with 929 entries.
This book is a must-have for those interested in the full breadth of research done in the biological & computer vision community. As a bonus, the chapters can also be used in a seminar-based, advanced undergraduate course in mathematical based computer vision.


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This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the theoretical efforts of mathematicians and engineers. This marriage of mathematics and computer vision - computational vision - has resulted in a discrete approach to image processing that is more reliable when leveraging in practical tasks. This comprehensive volume provides a detailed discourse on the mathematical models used in computational vision from leading educators and active research experts in this field. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis.Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.

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Reliable Face Recognition Methods: System Design, Implementation and Evaluation (International Series on Biometrics) Review

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(More customer reviews)The problem tackled by this book is buried deep in evolution. It is one of the most basic properties of an organism that it reliably recognise others of its species. For reproduction, if nothing else. Humans have the added twist that facial recognition is important, for the extra reasons that the face is usually hairless and has many expressive muscles.
But the way the mind does its analysis is still largely opaque. By contrast, most of the book's methods look at it via other approaches. Signal processing with many custom features specific to faces. The chapters show impressive current capabilities. However, they also readily indicate the limitations. And suggest possible future improvements.

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This book seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor. These include neurosciences, statistics, signal and image processing, computer vision, machine learning and data mining. The book examines the evolution of research surrounding the field to date, explores new directions, and offers specific guidance on the most promising venues for future research and development. The book's focused approach and its clarity of presentation make this an excellent reference work.

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

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(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.

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

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Numerical Geometry of Non-Rigid Shapes (Monographs in Computer Science) Review

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(More customer reviews)Numerical geometry of non-rigid shapes is the first attempt to present a focused and broad study of topics in non-rigid shape analysis. The book presents theoretical foundations, methods, algorithms and applications involving non-rigid shapes in different fields including computer vision, pattern recognition, and computer graphics. A special focus is made on practical value of the book - it is accompanied with code examples and references to commercial and public-domain software. Recommended as a textbook for computer vision and pattern recognition courses, reference for students and experts in the field.

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Deformable objects are ubiquitous in the world surrounding us, on all levels from micro to macro. The need to study such shapes and model their behavior arises in a wide spectrum of applications, ranging from medicine to security. In recent years, non-rigid shapes have attracted growing interest, which has led to rapid development of the field, where state-of-the-art results from very different sciences - theoretical and numerical geometry, optimization, linear algebra, graph theory, machine learning and computer graphics, to mention several - are applied to find solutions.This book gives an overview of the current state of science in analysis and synthesis of non-rigid shapes. Everyday examples are used to explain concepts and to illustrate different techniques. The presentation unfolds systematically and numerous figures enrich the engaging exposition. Practice problems follow at the end of each chapter, with detailed solutions to selected problems in the appendix. A gallery of colored images enhances the text.This book will be of interest to graduate students, researchers and professionals in different fields of mathematics, computer science and engineering. It may be used for courses in computer vision, numerical geometry and geometric modeling and computer graphics or for self-study.

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Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability) Review

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(More customer reviews)This is absolute a bible book for any person who want to learn Gibbs sampler and simulated annealing seriously. The format of this book, though full of mathematical equations, is very self-evident and concise. Nothing is missing and nothing is redundent. It is an enjoyable journey to follow the logic and principle in this book, with all your attention in. There are full of in-depth discussion in all aspect of the Gibbs sampler, simulated annealing, from the visiting scheme to cooling schedule, and parallel algorithms. The references are excellent too. The author seems to have read all publications till 1995 about this topic and give an excellent detailed and in-depth survey in his book. At the end of your reading, you would have love the mathematical form the author used. Without these tools, many discussions in this book will be just impossible and groundless. I personally have read this book for several times.

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"This book is concerned with a probabilistic approach for image analysis, mostly from the Bayesian point of view, and the important Markov chain Monte Carlo methods commonly used....This book will be useful, especially to researchers with a strong background in probability and an interest in image analysis. The author has presented the theory with rigor'he doesn't neglect applications, providing numerous examples of applications to illustrate the theory." -- MATHEMATICAL REVIEWS

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Robotics: Modelling, Planning and Control (Advanced Textbooks in Control and Signal Processing) Review

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(More customer reviews)This book has everything a new grad student or aspiring roboticist would want. It covers the mechanics, the controls and even some vision for robotic manipulators. Very nice resource.

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Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained.The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors.To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.

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Statistics and Analysis of Shapes (Modeling and Simulation in Science, Engineering and Technology) Review

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(More customer reviews)Automated shape analysis is a difficult task, with many practical applications. The text is a collection of papers tackling the problem. Several use level set methods while others perform analysis in an infinite dimensional space. Some chapters deal with extracting level lines from a two dimensional image, while one chapter discusses the use of integral invariants for shape matching.
The maths treatment throughout the book is advanced. Perhaps best for a graduate student or higher. What may be striking to the reader is that our wetware pattern recognition is still so much better than the algorithmic methods documented here. Much research remains to be done, and the book is only an early step.

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The subject of pattern analysis and recognition pervades many aspects of our daily lives, including user authentication in banking, object retrieval from databases in the consumer sector, and the omnipresent surveillance and security measures around sensitive areas. Shape analysis, a fundamental building block in many approaches to these applications, is also used in statistics, biomedical applications (Magnetic Resonance Imaging), and many other related disciplines.With contributions from some of the leading experts and pioneers in the field, this self-contained, unified volume is the first comprehensive treatment of theory, methods, and algorithms available in a single resource. Developments are discussed from a rapidly increasing number of research papers in diverse fields, including the mathematical and physical sciences, engineering, and medicine.

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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Professor Gelman has edited a book containing 29 articles dealing primarily with real applications of Bayesian methods for causal inference and the treatment of incomplete data.
It contains a collection of the best work in applied statistics by prominent statisticians. In addition to learning the wide variety of problems that have been solved using the Bayesian approach (particularly in the medical field) the reader can learn and appreciate the power and ease of interpretation of Bayesian results.

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This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.
Key features of the book include:
Comprehensive coverage of an imporant area for both research and applications.
Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
Includes a number of applications from the social and health sciences.
Edited and authored by highly respected researchers in the area.

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Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication Review

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(More customer reviews)After the 9/11 terrorist attack using commercial airplanes to crash into the pentagon and the world trade center buildings, statisticians among other found their way into developing tools to deal with the such problems. This book is an edited volume of of statistical researchers effort at solving some specific problems addressing potential terrorist attacks and finding ways to prevent future occurrences. Of the topics included are 1. game theory and risk taking, 2. biometric authentication (a pattern recognition technique for matching signatures, 3. matching faces for identification of terriorists using fake identification, 4. and DNA among other things.
Many well known statisticians from RAND, the Naval Postgraduate School, the National Institute of Science and Technology, Los Alamos National Laboratory and some academic institutions present papers on a variety of topics including game theory, biometric authentification, aggregated algorithms, syndromic surveillance, pattern recognition, simulation studies and other counterterrorism approaches.
This book is the first of its kind and it really shows how statistics can play a major role as analytic tools for counterterrorism.

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With the realization that many clues and hints preceded the September 11 terrorist attacks, statisticians became an important part of the global war on terror. This book surveys emerging research at the intersection of national security and statistical sciences. In it, a diverse group of talented researchers address such topics as Syndromic Surveillance; Modeling and Simulation; Biometric Authentication; and Game Theory. The book includes general reviews of quantitative approaches to counterterrorism, for decision makers with policy backgrounds, as well as technical treatments of statistical issues that will appeal to quantitative researchers.

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