This article develops upper and lower bounds for the probability of Boolean functions by
treating multiple occurrences of variables as independent and assigning them new …

We study the evaluation of positive conjunctive queries with Boolean aggregate tests
(similar to HAVING in SQL) on probabilistic databases. More precisely, we study conjunctive …

Probabilistic databases (PDBs) introduce uncertainty into relational databases by specifying
probabilities for several possible instances. Traditionally, they are finite probability spaces …

We consider an unreliabable database as a random variable defined from a relational
database with various probabilistic models. For a given query Q, we define its reliability on a …

Large-scale probabilistic knowledge bases are becoming increasingly important in
academia and industry. They are continuously extended with new data, powered by modern …

Threshold machines are Turing machines whose acceptance is determined by what portion
of the machine's computation paths are accepting paths. Probabilistic machines are Turing …

We consider two probability distributions on Boolean functions defined in terms of their
representations by and/or trees (or formulas). The relationships between them, and …

Conceptually, the common approach to manipulating probabilistic data is to evaluate
relational queries and then calculate the probability of each tuple in the result. This …

We study the uniform query reliability problem, which asks, for a fixed Boolean query Q,
given an instance I, how many subinstances of I satisfy Q. Equivalently, this is a restricted …

We consider the problem of learning monotone Boolean functions over {0, 1}/sup n/under
the uniform distribution. Specifically, given a polynomial number of uniform random samples …