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Scandinavian Journal of Statistics, 2003
AbstractA class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space.
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AbstractA class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space.
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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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1995
Abstract The aim of this chapter is to provide a non-technical overview of graphical modelling. Independence graphs with both lines (undirected edges) and arrows (directed edges) are described, together with associated models in duding both discrete and continuous variables. Some discussion of causal inference is also given.
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Abstract The aim of this chapter is to provide a non-technical overview of graphical modelling. Independence graphs with both lines (undirected edges) and arrows (directed edges) are described, together with associated models in duding both discrete and continuous variables. Some discussion of causal inference is also given.
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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.
La Rocca, Luca, Roverato, Alberto
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2001
This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.
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This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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