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A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat” data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much of the relational structure present in our database. This paper builds on the(More)
Gene duplication and loss is a powerful source of functional innovation. However, the general principles that govern this process are still largely unknown. With the growing number of sequenced genomes, it is now possible to examine these events in a comprehensive and unbiased manner. Here, we develop a procedure that resolves the evolutionary history of(More)
This paper introduces the Generalized Search Tree (GiST), an index structure supporting an extensible set of queries and data types. The GiST allows new data types to be indexed in a manner supporting queries natural to the types; this is in contrast to previous work on tree extensibility which only supported the traditional set of equality and range(More)
Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper,(More)
Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS’s) and Bayesian networks (BNs). FRS’s provide an excellent representation for the organizational structure of large complex domains, but their applicability is limited because of their inability to deal with uncertainty and noise. BNs(More)
The requirements of wide-area distributed database systems differ dramatically from those of local-area network systems. In a wide-area network (WAN) configuration, individual sites usually report to different system administrators, have different access and charging algorithms, install site-specific data type extensions, and have different constraints on(More)
In a rational programming language, a program specifies a situation faced by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the situation. This paper presents IBAL, a rational programming language for probabilistic and decision-theoretic agents. IBAL provides a rich declarative language for describing(More)
Probability distributions are useful for expressing the meanings of probabilistic languages, which support formal modeling of and reasoning about uncertainty. Probability distributions form a monad, and the monadic definition leads to a simple, natural semantics for a stochastic lambda calculus, as well as simple, clean implementations of common queries.(More)
A system with multiple interacting agents (whether artiicial or human) is often best analyzed using game-theoretic tools. Unfortunately, while the formal foundations are well-established, standard computational techniques for game-theoretic reasoning are inadequate for dealing with realistic games. This paper describes the Gala system, an implemented system(More)