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

: Several major advances in the area of combinatorial algorithms include improved algorithms for matrix multiplication and maximum network flow, a polynomial-time algorithm for linear programming, and steps toward a polynomial-time algorithm for graph isomorphism. This paper surveys these results and suggests directions for future research. Included is a discussion of recent work by the author and his students on dynamic dictionaries, network flow problems, and related questions.

Recent Developments in the Complexity of Combinatorial Algorithms

Let T be a binary search tree of n nodes with root r, left subtree $L=\text {left}(r)$, and right subtree $R=\text {right}(r)$. The preorder and postorder of T are defined as follows: the preorder and postorder of the empty tree is the empty sequence, and 

$$\begin{aligned} \text {preorder}(T)&= (r)\oplus \text {preorder}(L)\oplus \text {preorder}(R)\\ \text {postorder}(T)&= \text {postorder}(L)\oplus \text {postorder}(R)\oplus (r), \end{aligned}$$

where $\oplus $ denotes sequence concatenation. (We will refer to any such sequence as a preorder or a postorder). We prove the following results about the behavior of splaying [21] preorders and postorders:

1.

Inserting the nodes of preorder(T) into an empty tree via splaying costs O(n). (Theorem 2.)




2.

Inserting the nodes of postorder(T) into an empty tree via splaying costs O(n). (Theorem 3.)




3.

If $T'$ has the same keys as T and T is weight-balanced [18] then splaying either preorder(T) or postorder(T) starting from $T'$ costs O(n). (Theorem 4.)

Splaying Preorders and Postorders

Plural sets of items corresponding to plural tiers of a multi-tiered system are defined, where the items represent amounts of resources to allocate to the tiers. Items from respective sets are selected based on a response time constraint and a target cost. Information is provided pertaining to allocation of amounts of resources to the respective tiers based on the selected items.

Allocation of resources for tiers of a multi-tiered system based on selecting items from respective sets

We introduce the hollow heap, a very simple data structure with the same amortized efficiency as the classical Fibonacci heap All heap operations except delete and delete-min take $O(1)$ time, worst case as well as amortized; delete and delete-min take $O(\log n)$ amortized time on a heap of $n$ items Hollow heaps are by far the simplest structure to achieve this Hollow heaps combine two novel ideas: the use of lazy deletion and re-insertion to do decrease-key operations, and the use of a dag (directed acyclic graph) instead of a tree or set of trees to represent a heap Lazy deletion produces hollow nodes (nodes without items), giving the data structure its name

Robert E. Tarjan

Papers

Recent Developments in the Complexity of Combinatorial Algorithms

Splaying Preorders and Postorders

Allocation of resources for tiers of a multi-tiered system based on selecting items from respective sets

Hollow Heaps

Amortized rotation cost in AVL trees