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TABOR: A Graphical Model-based Approach for Anomaly Detection in Industrial Control Systems
ACM Asia Conference on Computer and Communications Security, 2018Industrial Control Systems (ICS) such as water and power are critical to any society. Process anomaly detection mechanisms have been proposed to protect such systems to minimize the risk of damage or loss of resources.
Qin Lin +3 more
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A Directed Sparse Graphical Model for Multi-target Tracking
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018We propose a Directed Sparse Graphical Model (DSGM) for multi-target tracking. In the category of global optimization for multi-target tracking, traditional approaches have two main drawbacks.
M. Ullah, F. A. Cheikh
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Model selection and estimation in the Gaussian graphical model
, 2007We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead to a sparse and shrinkage estimator of the concentration matrix that is positive definite, and thus conduct model selection ...
M. Yuan, Yi Lin
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A Gaussian graphical model approach to climate networks.
Chaos, 2014Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid.
T. Zerenner +3 more
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Mathematical Models of Graphics
Computer Graphics and Image Processing, 1980Publisher Summary This chapter reviews some mathematical models for graphics as data sources and the interplay between these models and coding schemes. Graphics include the following three classes of images: (1) images that are nominally two tone, (2) binary images derived from continuous-tone images, (3) and binary images created in some continuous ...
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1996
Abstract The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables.
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Abstract The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables.
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2012
Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years.
Højsgaard, Søren +2 more
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Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years.
Højsgaard, Søren +2 more
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2001
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes and edges.
Jensen, Finn Verner +1 more
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Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes and edges.
Jensen, Finn Verner +1 more
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Discrete Graphical Models and Their Parameterization
2018This chapter is devoted to graphical models in which the observed variables are categorical, that is, whose state space consists of a finite number of values. The focus is on regression graph models, because this family of models allows us to approach discrete graphical models with a sufficient degree of generality.
Steffen L. Lauritzen +3 more
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2015
This chapter gives an introduction to causal modeling, in particular to causal Bayesian networks. It starts by introducing causal models and their importance. Then causal Bayesian networks are described, including two types of causal reasoning, prediction and counterfactuals.
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This chapter gives an introduction to causal modeling, in particular to causal Bayesian networks. It starts by introducing causal models and their importance. Then causal Bayesian networks are described, including two types of causal reasoning, prediction and counterfactuals.
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