2010 14th IEEE International Enterprise Distributed Object Computing Conference 2010
DOI: 10.1109/edoc.2010.29
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Hybrid Probabilistic Relational Models for System Quality Analysis

Abstract: The formalism Probabilistic Relational Models (PRM) couples discrete Bayesian Networks with a modeling formalism similar to UML class diagrams and has been used for architecture analysis. PRMs are well-suited to perform architecture analysis with respect to system qualities since they support both modeling and analysis within the same formalism. A particular strength of PRMs is the ability to perform meaningful analysis of domains where there is a high level of uncertainty, as is often the case when performing… Show more

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Cited by 13 publications
(13 citation statements)
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“…There are several formalisms that can represent hybrid relational domains including Hybrid Markov Logic Networks (HMLNs) (Wang and Domingos 2008), Hybrid Problog (HProblog) (Gutmann et al 2011), Continuous Bayesian Logic Programs (CBLPs) (Kersting and De Raedt 2001), Learning Modulo Theories (LMT) (Teso et al 2013) and Hybrid Probabilistic Relational Models (HPRMs) (Narman et al 2010). Additionally, formalisms such as Relational Continuous Models (RCMs) (Choi et al 2010) and Gaussian Logic (Kuželka et al 2011) can model domains that exclusively contain continuous variables.…”
Section: Results On the Pkdd'99 Financial Data Setmentioning
confidence: 99%
See 1 more Smart Citation
“…There are several formalisms that can represent hybrid relational domains including Hybrid Markov Logic Networks (HMLNs) (Wang and Domingos 2008), Hybrid Problog (HProblog) (Gutmann et al 2011), Continuous Bayesian Logic Programs (CBLPs) (Kersting and De Raedt 2001), Learning Modulo Theories (LMT) (Teso et al 2013) and Hybrid Probabilistic Relational Models (HPRMs) (Narman et al 2010). Additionally, formalisms such as Relational Continuous Models (RCMs) (Choi et al 2010) and Gaussian Logic (Kuželka et al 2011) can model domains that exclusively contain continuous variables.…”
Section: Results On the Pkdd'99 Financial Data Setmentioning
confidence: 99%
“…To address this shortcoming, there has recently been increased interest in designing hybrid SRL formalisms such as Hybrid Markov Logic Networks (HMLNs) (Wang and Domingos 2008), Hybrid ProbLog (HProbLog) (Gutmann et al 2011), Continuous Bayesian Logic Programs (CBLPs) (Kersting and De Raedt 2001), Learning Modulo Theories (LMT) (Teso et al 2013) and Hybrid Probabilistic Relational Models (HPRMs) (Narman et al 2010). The vast majority of the work on hybrid SRL has focused on two issues.…”
Section: Introductionmentioning
confidence: 99%
“…The ergodicity of a system as common manufacturing plants workflows can present high optimization levels due to presence of discrete variable within the system and control methods over the agents. However, this situation is not a constant expression considering some real-life situations or hybrid organizations where the production line is composed of a human presence as a metric of productivity [11][12][13]. Therefore, for the purpose of empirical investigation of these types of organizations, it is recommended starting from an analysis in which non-ergodicity is the a priori event, more present in the real world, where the distribution m of labor performances expressed by an agent assumes various possible forms (derivations) [13][14][15][16][17].…”
Section: Method: Productivity Equationmentioning
confidence: 99%
“…Continuous BLP (Kersting and Raedt 2001) and Hybrid PRM (Narman et al 2010) extend their base models by using Hybrid Bayesian Networks (Murphy 1998). Hybrid MLN (Wang and Domingos 2008) allows description of continuous properties and attributes (e.g., the formula length(x) = 5 with weight w) deriving MRFs with continuous-valued nodes (e.g., length(a) for a grounding of x, with mean 5 and standard deviation 1/ √ 2w).…”
Section: Related Workmentioning
confidence: 99%