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

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.

/pdf/bayesian-networks-and-decision-graphs-3d5m8xvghj.pdf

Bayesian networks and decision graphs

https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1988.tb01721.x

Local computations with probabilities on graphical structures and their application to expert systems

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

/pdf/data-integration-a-theoretical-perspective-58aesjvhuk.pdf

Data integration: a theoretical perspective

This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. Coverage of emerging topics such as data streams and cloud computing Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.

Principles of Distributed Database Systems

A class of database schemes, called acychc, was recently introduced. It is shown that this class has a number of desirable properties. In particular, several desirable properties that have been studied by other researchers m very different terms are all shown to be eqmvalent to acydicity. In addition, several equivalent charactenzauons of the class m terms of graphs and hypergraphs are given, and a smaple algorithm for determining acychclty is presented. Also given are several eqmvalent characterizations of those sets M of multivalued dependencies such that M is the set of muRlvalued dependencies that are the consequences of a given join dependency. Several characterizations for a conflict-free (in the sense of Lien) set of muluvalued dependencies are provided.

On the Desirability of Acyclic Database Schemes

This paper considers the efficient evaluation of recursive queries expressed using Horn Clauses. We define sideways information passing formally and show how a query evaluation algorithm may be defined in terms of sideways information passing and control. We then consider a class of information passing strategies which suffices to describe most query evaluation algorithms in the database literature, and show that these strategies may always be implemented by rewriting a given program and evaluating the rewritten program bottom-up. We describe in detail several algorithms for rewriting a program. These algorithms generalize the Counting and Magic Sets algorithms to work with arbitrary programs. Safety and optimality of the algorithms are also considered.

On the power of magic

Problems related to functional dependencies and the algorithmic design of relational schemas are examined. Specifically, the following results are presented: (1) a tree model of derivations of functional dependencies from other functional dependencies; (2) a linear-time algorithm to test if a functional dependency is in the closure of a set of functional dependencies; (3) a quadratic-time implementation of Bernstein's third normal form schema synthesis algorithm.Furthermore, it is shown that most interesting algorithmic questions about Boyce-Codd normal form and keys are NP-complete and are therefore probably not amenable to fast algorithmic solutions.

Computational problems related to the design of normal form relational schemas

Definition d'une classe de dependances, dependances generant l'equalite et les triples et generalisation du processus de poursuite pour traiter ces dependances. On montre quelques cas pour lesquels il y a une procedure de decision. Redondance de l'egalite pour decider de l'implication des dependances generant les triples et «presque redondance» pour decider de l'implication des dependances generant l'egalite

A Proof Procedure for Data Dependencies

In this paper we study the implication and the finite implication problems for data dependencies. When all dependencies are total the problems are equivalent and solvable but are NP-hard, i.e., probably computationally intractable. For non-total dependencies the implication problem is unsolvable, and the finite implication problem is not even partially solvable. Thus, there can be no formal system for finite implication. The meta decision problems of deciding for a given class of dependencies whether the implication problem is solvable or whether implication is equivalent to finite implication are also unsolvable.

/pdf/the-implication-problem-for-data-dependencies-2ibb69efuq.pdf

Catriel Beeri

Papers

On the Desirability of Acyclic Database Schemes

On the power of magic

Computational problems related to the design of normal form relational schemas

A Proof Procedure for Data Dependencies

The Implication Problem for Data Dependencies