Results 31 to 40 of about 343,485 (273)
Probabilistic Graph Models (PGMs) for Feature Selection in Time Series Analysis and Forecasting
JISR on Computing, 2021 Time series or longitudinal analysis has a very important aspect in the field of research. Day by day new and better analyses are getting developed in this field.
Syed Ali Raza Naqvi
doaj +1 more source
Testing conditional independence using maximal nonlinear conditional correlation
, 2010 In this paper, the maximal nonlinear conditional correlation of two random vectors $X$ and $Y$ given another random vector $Z$, denoted by $\rho_1(X,Y|Z)$, is defined as a measure of conditional association, which satisfies certain desirable properties ...
Huang, Tzee-Ming
core +1 more source
Directed expected utility networks [PDF]
, 2016 A variety of statistical graphical models have been defined to represent the conditional independences underlying a random vector of interest. Similarly, many different graphs embedding various types of preferential independences, such as, for example ...
Leonelli, Manuele, Smith, Jim Q.
core +3 more sources
Gaussian Covariance Faithful Markov Trees
Journal of Probability and Statistics, 2011 Graphical models are useful for characterizing conditional and marginal independence structures in high-dimensional distributions. An important class of graphical models is covariance graph models, where the nodes of a graph represent different ...
Dhafer Malouche, Bala Rajaratnam
doaj +1 more source
The Strucplot Framework: Visualizing Multi-way Contingency Tables with vcd
Journal of Statistical Software, 2006 This paper describes the "strucplot" framework for the visualization of multi-way contingency tables. Strucplot displays include hierarchical conditional plots such as mosaic, association, and sieve plots, and can be combined into more complex ...
David Meyer, Achim Zeileis, Kurt Hornik
doaj +1 more source
Computational Test for Conditional Independence
AlgorithmsConditional Independence (CI) testing is fundamental in statistical analysis. For example, CI testing helps validate causal graphs or longitudinal data analysis with repeated measures in causal inference.
Christian B. H. Thorjussen +3 more
doaj +1 more source
Determining full conditional independence by low-order conditioning [PDF]
, 2009 A concentration graph associated with a random vector is an undirected graph where each vertex corresponds to one random variable in the vector. The absence of an edge between any pair of vertices (or variables) is equivalent to full conditional ...
Malouche, Dhafer
core +3 more sources
Algebraic Aspects of Conditional Independence and Graphical Models
, 2017 This chapter of the forthcoming Handbook of Graphical Models contains an overview of basic theorems and techniques from algebraic geometry and how they can be applied to the study of conditional independence and graphical models.
Kahle, Thomas +2 more
core +1 more source
Conditional independence in propositional logic
Artificial Intelligence, 2002 Independence -- the study of what is relevant to a given problem of reasoning -- is an important AI topic. In this paper, we investigate several notions of conditional independence in propositional logic: Darwiche and Pearl's conditional independence, and some more restricted forms of it.
Jerome Lang +2 more
openaire +4 more sources
$M^{4}CD$ : A Robust Change Detection Method for Intelligent Visual Surveillance
IEEE Access, 2018 In this paper, we propose a robust change detection method for intelligent visual surveillance. This method, named M4CD, includes three major steps. First, a sample-based background model that integrates color and texture cues is built and updated over ...
Kunfeng Wang, Chao Gou, Fei-Yue Wang
doaj +1 more source