Results 51 to 60 of about 22,057,993 (342)
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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raxmlGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML
Methods in Ecology and Evolution, 2020 raxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used softwares for phylogenetic inference using maximum likelihood. Here we present raxmlGUI 2.0, a complete rewrite of the GUI which seamlessly integrates RAxML binaries
Daniel Edler +3 more
semanticscholar +1 more source
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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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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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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Graphical model for joint segmentation and tracking of multiple dividing cells
Bioinform., 2015 MOTIVATION To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, so-called tracking-by-assignment methods are flexible ...
Martin Schiegg +5 more
semanticscholar +1 more source
Learning the Structure of Hub Network Based on Graph Model
Xibei Gongye Daxue Xuebao, 2019 In this paper, we focus on the structure learning problem of the hub network. In the neighborhood selection framework, we use the L1 and L2 regularizers to incorporate the sparse and group prior of the hub network, so as to make the network easier to ...
doaj +1 more source
Mathematics, 2022 Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the ...
Rui Luo +4 more
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Compressive Nonparametric Graphical Model Selection For Time Series
, 2014 We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian vector random process from finite-length observations.
Bölcskei, Helmut +3 more
core +1 more source
Sparse Nonparametric Graphical Models [PDF]
Statistical Science, 2012 Published in at http://dx.doi.org/10.1214/12-STS391 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Lafferty, John +2 more
openaire +4 more sources