Learning Probabilistic Relational Models

Friedman, Nir; Getoor, Lise; Koller, Daphne; Pfeffer, Avi

doi:10.1007/978-3-662-04599-2_13

Cited by 399 publications

(255 citation statements)

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“…We merely use an abstract syntax called as First-Order Conditional Influence (FOCI) statements [13] to present the semantics of the directed models. We had earlier used this syntax to derive learning algorithms [13] and showed how most directed models such as BLPs [10], RBNs [7], PRMs [3], probabilistic relational language [4] and logical Bayes nets [2] can be represented using this syntax. The goal of this work is not to convert from FOCI statements to MLNs but to show that the knowledge captured by directed models can be represented using MLNs.…”

Section: Mlns and Directed Modelsmentioning

confidence: 99%

“…Directed models can learn conditional distributions due to each of the causes separately and combine them using a (possibly stochastic) function, thus making the process of learning easier. This notion of ICI has been extended to directed SRL models in two different ways: while PRMs [3] use aggregators such as max, min, and average to combine the influences due to several parents, other formalisms such as BLPs [10] and RBNs [8] use combination functions such as Noisy-OR, mean, or weighted mean to combine distributions.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models

Natarajan

Khot

Lowd³

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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Abstract.A new method is proposed for compiling causal independencies into Markov logic networks (MLNs). An MLN can be viewed as compactly representing a factorization of a joint probability into the product of a set of factors guided by logical formulas. We present a notion of causal independence that enables one to further factorize the factors into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The causal independence lets us specify the factor in terms of weighted, directed clauses and operators, such as "or", "sum" or "max", on the contribution of the variables involved in the factors, hence combining both undirected and directed knowledge. Our experimental evaluations shows that making use of the finer-grain factorization provided by causal independence can improve quality of parameter learning in MLNs.

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Section: Mlns and Directed Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models

Natarajan

Khot

Lowd³

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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“…In (Getoor, Friedman, Koller, & Pfeffer, 2001) the concept of relational schema and relational skeleton are introduced. A relational skeleton for a relational schema is a partial specification (instantiation) of the schema: it specifies the objects involved in the schema and the relations between them.…”

Section: Appendix Bmentioning

confidence: 99%

“…A relational skeleton for a relational schema is a partial specification (instantiation) of the schema: it specifies the objects involved in the schema and the relations between them. In (Getoor et al, 2001) Getoor et al, define a K-PRM as the RBN that specifies the probability distribution over particular instantiations of a given skeleton: for each schema there are more skeletons, for each skeleton there are more completions (i.e., complete instantiations). A K-PRM induces a distribution over instances that complete the skeleton.…”

Section: Appendix Bmentioning

confidence: 99%

Modeling and Inference with Relational Dynamic Bayesian Networks

Manfredotti

2009

Advances in Artificial Intelligence

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AcknowledgementsPrima di partire per un lungo viaggio devi portare con te la voglia di non tornare piu'.Irene Grandi from the song "Prima di partire per un lungo viaggio".Molti (maligni!) penseranno che la citazione si riferisce al mio non voler mai piu' tornare in Bicocca ... Ebbene no! Il dottorato e' stato un viaggio e come e' giusto, in quanto viaggio, l'ho percorso "gustandomelo".Il dottorato e' stata un'esperienza molto costruttiva e la crescita personale e di ricercatore che ne ho derivato non e' stata tutta merito mio ma, soprattutto, delle persone che mi hanno indirizzato e accompagnato in questo cammino e che ora mi piacerebbe ringraziare una per una.Prima tra tutte la mia relatrice la professoressa Messina ("La Prof"). Ha saputo consigliarmi sulla strada da prendere e, quando mi perdevo, ha saputo sempre guidarmi verso il raggiungimento dei miei obiettivi. La collaborazione con lei e' stata un'importante occasione di crescita per me."A braccetto" con La Prof non puo' mancare il professor Archetti. Sempre presente anche se spesso non fisicamente. Tante delle idee presenti in questa tesi sono nate dalla discussione con lui, da sue proposte e dalla sua voglia di "provare".Nel professor Sorrenti ho sempre trovato un confidente e un amico, ma anche una persona sul cui giudizio potevo contare e della cui opinione potevo fidarmi. Grazie! I tre anni di dottorato sono stati tre anni pieni di amici incontrati lungo questo cammino: Dario, Yuri (che ha consegnato la mia domanda di tesi, grazie!) Simone, Sergio, i due Daniele (che poi ho ritrovato a Toronto), Sara (Un amico e' per sempre!), Elena, Daniele (che per primo mi ha spiegato particle filter), Axel (che ora iniziera' questo "viaggio"), Paolo, Davide e tanti altri con i quali ho condiviso tanti momenti di scherzi, di pausa, di chiacchera e anche a volte di ..., grazie a tutti, ragazzi! Ma piu' di tutti devo ringraziare le mie due colleghe e compagne di (s)ventura: Elisabetta e Ilaria. Ne abbiamo passate tante insieme, lavorativamente parlando e non solo, ma i miei ringraziamenti sono soprattutto per questo ultimo anno e mezzo in cui, se pur lontana, mi avete sempre e comunque fatto sentire una di voi. Grazie! Il MIND (ex LIFE) e' stato un bellissimo gruppo nel quale lavorare (e per ognuno degli studenti passati di li' conservo un ricordo particolare) ma c'e' un altro gruppo che mi ha accolta e fatta sentire a casa in quest ultimo periodo: COGS alla UofT. Per farmi capire anche da loro cambio lingua per un attimo. IVAt UofT I found a great professor, Craig, who always gave me the right advice about the right thing to do and, more importantly, who introduced me to David, whose ideas and suggestions (during our long meetings) arrived at the right moment, a turning point for this work.But I also found a lot of colleagues and friends always there when I need something. I remember the long lunches with Laurent, that made me feeling like at home, the small talks with Kevin who supported me a lot, the "strange" conversations with Tyler and the support of Mark and ...

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“…These decision trees correspond to a particular form of ICL rules. -Unlike many representations such as Probabilistic Relational Models [Getoor et al, 2001], the ICL is not restricted to a fixed number of parameters to learn; it is possible to have representations where each individual has associated parameters. This should allow for richer representations and so better fit to the data.…”

Section: Icl and Learningmentioning

confidence: 99%

The Independent Choice Logic and Beyond

Poole

2008

Probabilistic Inductive Logic Programming

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Abstract. The Independent Choice Logic began in the early 90's as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.

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Learning Probabilistic Relational Models

Cited by 399 publications

References 9 publications

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models

Modeling and Inference with Relational Dynamic Bayesian Networks

The Independent Choice Logic and Beyond

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