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Universal Relations for Nonsolvable Statistical Models

G. Benfatto, P. Falco, V. Mastropietro
2010 Physical Review Letters  
We present the first rigorous derivation of a number of universal relations for a class of models with continuously varying indices (among which are interacting planar Ising models, quantum spin chains  ...  Most of these formulas were conjectured by Luther and Peschel, Kadanoff, Haldane, but only checked in the special solvable models; one of them, related to the anisotropic Ashkin-Teller model, is novel.  ...  It is well known that several models in statistical mechanics can be rewritten as coupled Ising models.  ... 
doi:10.1103/physrevlett.104.075701 pmid:20366897 fatcat:onf6hjf2knex7mapqpzgngcv2m

Using context with statistical relational models

Chen Wu, Hamid Aghajan
2009 Proceedings of the Workshop on Use of Context in Vision Processing - UCVP '09  
We demonstrate that Markov logic network provides a flexible way in the syntax of first-order logic to incorporate relational context information.  ...  It is also a probabilistic graphical model which handles uncertainty in the knowledge base, observations and decisions.  ...  In this paper we propose to use a statistical relational model -Markov logic network to incorporate user activity as context information for object recognition.  ... 
doi:10.1145/1722156.1722161 fatcat:q2lbkghi3vha3e33o2enjfder4

A statistical model for relational analysis

R. Duncan Luce, Josiah Macy, Renato Tagiuri
1955 Psychometrika  
Values obtained from two models with different assumptions are compared with empirical values. A simplified treatment is possible for groups with ten or more members.  ...  One such classification is given by relational analysis (2), a method developed in conjunction with a series of studies in interpersonal perception.  ...  Models such as these are essential for testing various hypotheses about interaction in the group since they provide a method for setting up and testing a null hypothesis by the usual statistical methods  ... 
doi:10.1007/bf02289038 fatcat:6ve3irot6vchtfnhrcbys7n3gi

Plan Recognition Using Statistical–Relational Models [chapter]

Sindhu Raghavan, Parag Singla, Raymond J. Mooney
2014 Plan, Activity, and Intent Recognition  
To overcome these limitations, we explore the application of statistical relational models that combine the strengths of both first-order logic and probabilistic graphical models to plan recognition.  ...  Most existing approaches to plan recognition and other abductive reasoning tasks either use first-order logic (or subsets of it) or probabilistic graphical models.  ...  The last decade has seen a rapid growth in the area of Statistical Relational Learning (SRL) (Getoor and Taskar, 2007) , which uses well-founded probabilistic methods while maintaining the representational  ... 
doi:10.1016/b978-0-12-398532-3.00003-8 fatcat:eevob56uqzafxndf55l7yzthou

Modelling relational statistics with Bayes Nets

Oliver Schulte, Hassan Khosravi, Arthur E. Kirkpatrick, Tianxiang Gao, Yuke Zhu
2013 Machine Learning  
Class-level 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?  ...  We represent class statistics using Parametrized Bayes Nets (PBNs), a first-order logic extension of Bayes nets.  ...  Acknowledgements Lise Getoor's work on Statistical Relational Models inspired us to consider class-level modelling with Parametrized Bayes nets; we thank her for helpful comments and encouragement.  ... 
doi:10.1007/s10994-013-5362-7 fatcat:yj7obxtsebc7bdqvrtsbthfcva

Statistical modeling of ground motion relations for seismic hazard analysis

Mathias Raschke
2013 Journal of Seismology  
We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics.  ...  assumptions, extreme value statistics).  ...  We search such a relation by a statistical analysis of the relations between the parameters of the GMRs and the magnitudes.  ... 
doi:10.1007/s10950-013-9386-z fatcat:xy7af7ubp5czholhrwfh7judya

Statistical Relational Learning With Unconventional String Models

Mai H. Vu, Ashkan Zehfroosh, Kristina Strother-Garcia, Michael Sebok, Jeffrey Heinz, Herbert G. Tanner
2018 Frontiers in Robotics and AI  
This paper shows how methods from statistical relational learning can be used to address problems in grammatical inference using model-theoretic representations of strings.  ...  Conventional representations include a binary relation for order and unary relations describing mutually exclusive properties of each position in the string.  ...  It is in this way that this work reveals connections between statistical relational learning, model theory, and grammatical inference.  ... 
doi:10.3389/frobt.2018.00076 pmid:33500955 pmcid:PMC7805770 fatcat:tbw7tlqfh5hkdj5pcakkceumxm

A Complete Characterization of Projectivity for Statistical Relational Models [article]

Manfred Jaeger, Oliver Schulte
2020 arXiv   pre-print
of projective relational models.  ...  In most existing statistical relational learning (SRL) frameworks, these models are not projective in the sense that the marginal of the distribution for size-n structures on induced sub-structures of  ...  Introduction Many types of generative models have been proposed for relational data in several fields, including machine learning and statistics.  ... 
arXiv:2004.10984v2 fatcat:kxn7iarz4rgw5ot5gyhv5odm2m

Graphical Models in a Nutshell [chapter]

2007 Introduction to Statistical Relational Learning  
The framework is quite general in that many of the commonly proposed statistical models (Kalman filters, hidden Markov models, Ising models) can be described as graphical models.  ...  Probabilistic graphical models are an elegant framework which combines uncertainty (probabilities) and logical structure (independence constraints) to compactly represent complex, real-world phenomena.  ...  This latter term relates to concepts from statistical physics, and it is the negative of what is referred to in that field as the Helmholtz free energy.  ... 
doi:10.7551/mitpress/7432.003.0004 fatcat:wbhjah7qczdftaiod5jg4ok2xe

Three-Dimensional Vertex Model Related BCC Model in Statistical Mechanics [article]

Zhan-Ning Hu
1995 arXiv   pre-print
This vertex model can be regard as a deformation of the one related the three-dimensional Baxter-Bazhanov model.  ...  In this paper, a three-dimensional vertex model is obtained. It is a duality of the three-dimensional integrable lattice model with N states proposed by Boos, Mangazeev, Sergeev and Stroganov.  ...  Introduction There is a large class of integrable lattice models in statistical mechanics.  ... 
arXiv:hep-th/9505007v1 fatcat:ldxp5ka5sfc4fm4spfirqktv34

Objective uncertainty relation with classical background in a statistical model

Agung Budiyono
2013 Physica A: Statistical Mechanics and its Applications  
We show within a statistical model of quantization reported in the previous work based on Hamilton-Jacobi theory with a random constraint that the statistics of fluctuations of the actual trajectories  ...  The relation is objective (observation independent) and implies the standard quantum mechanical uncertainty relation.  ...  The uncertainty relation thus directly reflects the kinematics of the statistical model of quantization.  ... 
doi:10.1016/j.physa.2012.08.012 fatcat:tdnwmds2ebek5jw65dnx3m7zb4

Relating probability amplitude mechanics to standard statistical models

Robert F. Bordley
1995 Physics Letters A  
Model he ft tht the heorem is so intuitive suggests tht the sme formul might e deduile from simpler sttistil modelsF es step towrd onstruting suh derivtionD note tht the proilities ssessed y the more  ...  the mount of unmesured vriilityD r goes to zeroD proility mplitude mehnis gives dditive proilitiesF rene we get simple orrespondene priniple etween lssil nd quntum mehnisF 2.3 Derivation from a Simple Statistical  ... 
doi:10.1016/0375-9601(95)00408-u fatcat:xttyf3xlfrhqbmggsmrted6yiu

Exploiting time-varying relationships in statistical relational models

Umang Sharan, Jennifer Neville
2007 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis - WebKDD/SNA-KDD '07  
In this paper, we present an initial approach to modeling dynamic relational data graphs in predictive models of attributes.  ...  there has been little effort to incorporate timevarying dependencies into relational models.  ...  This work attempts to model temporal dependencies by specifying a limited number of temporal patterns to moderate the relational dependencies.  ... 
doi:10.1145/1348549.1348551 fatcat:xslvgmobz5hipkjgsuolxzkhga

Using Related Languages to Enhance Statistical Language Models

Anna Currey, Alina Karakanta, Jon Dehdari
2016 Proceedings of the NAACL Student Research Workshop  
Finally, we integrate data from the related language into a translation model for a statistical machine translation application.  ...  and translation models with data from related languages.  ...  For statistical machine translation, our results show gains from augmenting the translation models of a low-resource language with transliterated related-language data.  ... 
doi:10.18653/v1/n16-2017 dblp:conf/naacl/CurreyKD16 fatcat:br76ld5jffcfjfwrmxrh3g2qia

Modeling of Statistical Relations in Regional Economy Sphere

Alexey S. Levizov, Inna F. Kurbyko
2012 Evropejskij Issledovatelʹ  
The present research represents the integrated system of processing of the empirical data on the basis of a combination of multivariate methods of mathematical statistics.  ... 
doaj:9b88cf462b8c4b96a1b49a73d33c1be3 fatcat:6qvk75swbfgzbmiuvratf242ca
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