From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

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Nonlinear dimensionality reduction by locally linear embedding.

From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.


0201000296B04062001

The Design and Analysis of Computer Algorithms

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

This paper describes a machine model intended to be useful in deriving realistic complexity bounds for tasks requiring list processing. As an example of the use of the model, the paper shows that any such machine requires non-linear time in the worst case to compute unions of disjoint sets on-line. All set union algorithms known to the author are instances of the model and are thus subject to the derived bound. One of the known algorithms achieves the bound to within a constant factor.

Reference machines require non-linear time to maintain disjoint sets

We present efficient data structures to maintain dynamic set of rectangles, each with priority assigned to it, such that we can efficiently find the rectangle of maximum priority containing a query point. Our data structures support insertions and deletions of rectangles. In one dimension, when rectangles are intervals, our most efficient data structure supports queries and insertions in O(log n) time, deletions in O(log n loglog n) time and requires linear space. When intervals are guaranteed to be nonoverlapping (but one can be nested within the other) we obtain a simpler data structure that supports all operations in O(log n) time.

Dynamic rectangular intersection with priorities

Gauss codes, planar Hamiltonian graphs, and stack-sortable permutations

This paper considers the problem of represmrtirrg stacks with catenation so that any stack, old or new, is available for access or update operations. Th]s problem arises in the implementation of list-based and functional programming languages. A solution is proposed requiring constant time and space for each stack operation except catenation, which requmes O(log log k) time and space. Here k is the number of stack operations done before the mtenation. All the resource bounds are amortized over the sequence of operations.

Fully persistent lists with catenation

Pattern matching in trees is fundamental to a variety of programming language systems. However, progress has been slow in satisfying a pressing need for general-purpose pattern-matching algorithms that are efficient in both time and space. We offer asymptotic improvements in both time and space to Chase's bottom-up algorithm for pattern preprocessing. A preliminary implementation of our algorithm runs ten times faster than Chase's (1987) implementation on the hardest problem instances. Our preprocessing algorithm has the advantage of being on-line with respect to pattern additions and deletions. It also adapts to favorable input instances, and on Hoffmann and O'Donnell's (1982) class of simple patterns, it performs better than their special-purpose algorithm tailored to this class. We show how to modify our algorithm using a new decomposition method to obtain a space/time tradeoff. Finally, we trade a log factor in time for a linear space bottom-up pattern-matching algorithm that handles a wide subclass of Hoffmann and O'Donnell's (1982) simple patterns.

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Robert E. Tarjan

Papers

Reference machines require non-linear time to maintain disjoint sets

Dynamic rectangular intersection with priorities

Gauss codes, planar Hamiltonian graphs, and stack-sortable permutations

Fully persistent lists with catenation

More efficient bottom-up multi-pattern matching in trees