Results 41 to 50 of about 1,244,068 (326)
Annals of Medicine, 2023 Background Probabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model.Objective To develop PGM for the prediction of clinical outcome in patients with ...
Dong Ah Shin +6 more
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Introduction to Graphical Modelling
arXiv: Machine Learning, 2010 The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov random fields) and Bayesian networks, which ...
Scutari, M, Strimmer, K
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Investigating effective wayfinding in airports: a Bayesian network approach
Transport, 2014 Effective wayfinding is the successful interplay of human and environmental factors resulting in a person successfully moving from their current position to a desired location in a timely manner. To date this process has not been modelled to reflect this
Anna Charisse Farr +4 more
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Stratified Gaussian graphical models [PDF]
Communications in Statistics - Theory and Methods, 2016 23 pages, 12 ...
Nyman, Henrik +2 more
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GRAPHICAL MODELS FOR CORRELATED DEFAULTS [PDF]
Mathematical Finance, 2012 A simple graphical model for correlated defaults is proposed, with explicit formulas for the loss distribution. Algebraic geometry techniques are employed to show that this model is well posed for default dependence: it represents any given marginal distribution for single firms and pairwise correlation matrix.
I. Onur Filiz +3 more
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Graphical Models for Genetic Analyses
Statistical Science, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lauritzen, S.L., Sheehan, Nuala A.
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Conditional Functional Graphical Models
Journal of the American Statistical Association, 2021 Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling.
Hongyu Zhao +4 more
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Mechanisms and kinetic assays of aminoacyl‐tRNA synthetases
FEBS Letters, EarlyView.Accurate protein synthesis is crucial for life. The key players are aminoacyl‐tRNA synthetases (AARSs), which read the genetic code by pairing cognate amino acids and tRNAs. AARSs establish high amino acid selectivity by employing physicochemical limits in molecular recognition.
Igor Zivkovic +2 more
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Active Learning for Undirected Graphical Model Selection [PDF]
, 2014 This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables.
Baraniuk, Richard G. +2 more
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Heterogeneous Reciprocal Graphical Models [PDF]
Biometrics, 2017 Summary We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs ...
Yang Ni +4 more
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