Learning Probabilistic Relational Models

By Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer. 1999

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Neural Logic Machines (2019, background): “Inductive logic programming (ILP) (Muggleton, 1991; 1996; Friedman et al., 1999) is a paradigm for learning logic rules derived from a limited set of rule templates from examples.”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “Approaches for link prediction include similarity based methods [13], [14], maximum likelihood models [15], [16], and probabilistic models [17],”
Applications of Probabilistic Programming (Master's thesis, 2015) (2016, methodology): “Our approach is also related to probabilistic model learning, for example to learning probabilistic relational models (Friedman et al., 1999) and Bayesian network structure (Mansinghka et al., 2012).”
UvA-DARE (Digital Academic Repository) Neural Relational Inference for Interacting Systems Neural Relational Inference for Interacting Systems (2018, unknown): “Statistical relational learning are based on probabilistic graphical models such as probabilistic relational model (Kemp & Tenenbaum, 2008; Getoor et al., 2001; Koller et al., 2007; Shum et al., 2019).”
Knowledge Graph Completion via Complex Tensor Factorization (2017, methodology): “First approaches to relational learning relied upon probabilistic graphical models (Getoor and Taskar, 2007), such as bayesian networks (Friedman et al., 1999) and markov logic networks (Richardson and Domingos, 2006).”
Relational Logistic Regression: The Directed Analog of Markov Logic Networks (2014, background): “…be discrete or continuous) to be when P(Q | X1, . . . ,Xn) can be factored into a product of non-negative pairwise factors and a non-negative factor for Q: P(Q | X1, . . . ,Xn) ∝ f0(Q) n ∏ i=1 fi(Q,Xi) where ∝ (proportional-to) means it is normalized separately for each assignment to the parents.”
Learning Probabilistic Models of Link Structure (2003, background): “The main exception to this rule has been the work on inductive logic programming (Muggleton, 1992, Lavrac̆ and Dz̆eroski, 1994). This link of work focuses on the problem of inferring link structure from a logical perspective. More recently, there has been a growing interest in combining the statisti”
Structured Priors for Structure Learning (2006, background): “At the other end of the flexibilitylearnability tradeoff, frameworks such as probabilistic relational models (PRMs) [Friedman et al., 1999] provide a far more expressive language for abstract relational knowledge that can constrain the space of Bayesian networks appropriate for a given domain, but i”
A Survey of Statistical Network Models (2009, background): “study, rather than being representative of the uncertainty of the relationships between nodes and node attributes. There is a multitude of literature on relational networks, e.g., see Friedman et al. [112], Getoor et al. [117], Neville and Jensen [229]; Neville et al. [230], and Getoor and Taskar [1”
Social Network Data Analytics (2011, methodology): “Among many approaches proposed within the SRL framework, two that have been directly applied to the node classi cation problem are Probabilistic Relational Models (PRMs) [10, 33] and Relational Markov Network Models (RMNs) [32].”
Graph based anomaly detection and description: a survey (2014, methodology): “As for the global methods, [Friedman et al., 1999] use probabilistic relational models (PRMs) as a (full joint) model and then use Loopy Belief Propagation (LBP) [Yedidia et al., 2003] for the inference.”
Graph Regularized Transductive Classification on Heterogeneous Information Networks (2010, methodology): “Through iteratively classifying an object by the majority class of its neighbors, this method performs very well compared to more complex models including Probabilistic Relational Models [6] [17], Relational Probability Trees [13] and Relational Bayesian Classifiers [14].”
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm (1999, background): “Similar cost issues occur in [14], where a variant of our algorithm is used for learning probabilistic models from relational databases.”