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99
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 816 (39 self)
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learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
A Tractable PseudoLikelihood Function for Bayes Nets Applied to Relational Data
"... Bayes nets (BNs) for relational databases are a major research topic in machine learning and artificial intelligence. When the database exhibits cyclic probabilistic dependencies, measuring the fit of a BN model to relational data with a likelihood function is a challenge [5, 36, 28, 9]. A common ap ..."
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Cited by 13 (10 self)
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approach to difficulties in defining a likelihood function is to employ a pseudolikelihood; a prominent example is the pseudo likelihood defined for Markov Logic Networks (MLNs). This paper proposes a new pseudo likelihood P ∗ for Parametrized Bayes Nets (PBNs) [32] and other relational versions of Bayes
Modeling of Hormone SecretionGenerating Mechanisms With Splines: A PseudoLikelihood Approach
"... Summary. A flexible and robust approach is proposed for the investigation of underlying hormone secretiongenerating mechanism. Characterizing hormone time series is a difficult task as most hormones are secreted in a pulsatile manner and pulses are often masked by the slow decay. We model hormone c ..."
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Cited by 2 (0 self)
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concentration as a filtered counting process where the intensity function of the counting process is modeled nonparametrically using periodic splines. The intensity function and parameters are estimated using a combination of weighted least squares and pseudolikelihood based on the first two moments. Our
Learning the structure of Markov logic networks
 In Proceedings of the 22nd International Conference on Machine Learning
, 2005
"... Markov logic networks (MLNs) combine logic and probability by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. In this paper we develop an algorithm for learning the structure of MLNs from relational databases, combining ideas from inductive l ..."
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Cited by 116 (21 self)
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logic programming (ILP) and feature induction in Markov networks. The algorithm performs a beam or shortestfirst search of the space of clauses, guided by a weighted pseudolikelihood measure. This requires computing the optimal weights for each candidate structure, but we show how this can be done
Pseudo Likelihood Estimation in Network Tomography
, 2003
"... Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today's fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely on the ..."
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Cited by 85 (4 self)
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on the collaboration of individual routers and servers to directly measure network traffic. Even though the aggregatory nature of possible network measurements gives rise to inverse problems, existing methods for solving inverse problems are usually computationally intractable or statistically inefficient.
DOI 10.1007/s1099400658331 Markov logic networks
, 2006
"... Abstract We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in ..."
Abstract
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learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
Modelling Relational Statistics With Bayes Nets
"... Abstract. Classlevel models capture relational statistics over object attributes and their connecting links, answering questions such as “what is the percentage of friendship pairs where both friends are women?” Classlevel relationships are important in themselves, and they support applications li ..."
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Cited by 6 (3 self)
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queries about specific ground facts. We propose a novel random selection semantics for PBNs, which does not make reference to a ground model, and supports classlevel queries. The parameters for this semantics can be learned using the recent pseudolikelihood measure [1] as the objective function
Estimation of TailRelated Risk Measures for Heteroscedastic Financial Time Series: an Extreme Value Approach
 Journal of Empirical Finance
, 1998
"... We propose a method for estimating VaR and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudomaximumlikelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) ..."
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Cited by 239 (6 self)
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We propose a method for estimating VaR and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudomaximumlikelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT
Maximum Pseudo Likelihood Estimation in Network Tomography
"... Abstract — Network monitoring and diagnosis are key to improving network performance. The difficulties of performance monitoring lie in today’s fast growing Internet, accompanied by increasingly heterogeneous and unregulated structures. Moreover, these tasks become even harder since one cannot rely ..."
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the pseudo loglikelihood function. Two examples with simulated or real data are used to illustrate the pseudo likelihood proposal: (1) inference of the internal link delay distributions through multicast endtoend measurements; (2) origindestination matrix estimation through link traffic counts. Index
Learning directed relational models with recursive dependencies
 IN: ILP
"... Recently, there has been an increasing interest in generative relational models that represent probabilistic patterns over both links and attributes. A key characteristic of relational data is that the value of a predicate often depends on values of the same predicate for related entities. In this ..."
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Cited by 4 (2 self)
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. In this paper we present a new approach to learning directed relational models which utilizes two key concepts: a pseudo likelihood measure that is well defined for recursive dependencies, and the notion of stratification from logic programming. An issue for modelling recursive dependencies with Bayes nets
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