Results 1  10
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392
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 819 (28 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical
Probabilistic Logic Programming
 In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI98
, 1998
"... . We present a new approach to probabilistic logic programs with a possible worlds semantics. Classical program clauses are extended by a subinterval of [0; 1] that describes the range for the conditional probability of the head of a clause given its body. We show that deduction in the defined proba ..."
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Cited by 62 (11 self)
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to probabilistic deduction that is efficient in interesting special cases. In the best case, the generated linear programs have a number of variables that is linear in the number of ground instances of purely probabilistic clauses in a probabilistic logic program. 1 INTRODUCTION There is already a quite extensive
On the Implementation of the Probabilistic Logic Programming Language ProbLog
 UNDER CONSIDERATION FOR PUBLICATION IN THEORY AND PRACTICE OF LOGIC PROGRAMMING
, 2003
"... The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networ ..."
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Cited by 41 (9 self)
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networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow
Inference in probabilistic logic programs with continuous random variables, Theory Pract
 Log. Program
"... ar ..."
Objectoriented Bayesian networks.
 In Proc. UAI97,
, 1997
"... Abstract Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of mediumscale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to re ..."
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Cited by 218 (9 self)
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to resemble the task of pro gramming using logical circuits. In this paper, we de scribe an objectoriented Bayesian network (OOBN) lan guage, which allows complex domains to be described in terms of interrelated objects. We use a Bayesian net work fragment to describe the probabilistic relations be tween
ProbLog: a probabilistic Prolog and its application in link discovery
 In Proceedings of 20th International Joint Conference on Artificial Intelligence
, 2007
"... We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is then defi ..."
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Cited by 144 (27 self)
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We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of Prob
Clp(bn): Constraint logic programming for probabilistic knowledge
 In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI03
, 2003
"... Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing v ..."
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Cited by 63 (7 self)
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Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing
Adaptive Bayesian logic programs
 PROCEEDINGS OF THE ELEVENTH CONFERENCE ON INDUCTIVE LOGIC PROGRAMMING (ILP01), VOLUME 2157 OF LNCS
, 2001
"... First order probabilistic logics combine a first order logic with a probabilistic knowledge representation. In this context, we introduce continuous Bayesian logic programs, which extend the recently introduced Bayesian logic programs to deal with continuous random variables. Bayesian logic programs ..."
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Cited by 30 (10 self)
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First order probabilistic logics combine a first order logic with a probabilistic knowledge representation. In this context, we introduce continuous Bayesian logic programs, which extend the recently introduced Bayesian logic programs to deal with continuous random variables. Bayesian logic
Firstorder incremental blockbased statistical timing analysis
 In DAC
, 2004
"... Variability in digital integrated circuits makes timing verification an extremely challenging task. In this paper, a canonical first order delay model is proposed that takes into account both correlated and independent randomness. A novel lineartime blockbased statistical timing algorithm is emplo ..."
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Cited by 193 (6 self)
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Variability in digital integrated circuits makes timing verification an extremely challenging task. In this paper, a canonical first order delay model is proposed that takes into account both correlated and independent randomness. A novel lineartime blockbased statistical timing algorithm
Adaptive MCMCBased Inference in Probabilistic Logic Programs
"... Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate technique ..."
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Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate
Results 1  10
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