A General Pairwise Comparison Model for Extremely Sparse Networks

A general unified framework for interval pairwise comparison matrices

Interval Pairwise Comparison Matrices have been widely used to account for uncertain statements concerning the preferences of decision makers. Several approaches have been proposed in the literature, such as multiplicative and fuzzy interval matrices. In this paper, we propose a general unified approach to Interval Pairwise Comparison Matrices, based on Abelian linearly ordered groups. In this ...

Deriving weights from general pairwise comparison matrices

The problem of deriving weights from pairwise comparison matrices has been treated extensively in the literature. Most of the results are devoted to the case when the matrix under consideration is reciprocally symmetric (i.e., the i, j-th element of the matrix is reciprocal to its j, i-th element for each i and j). However, there are some applications of the framework when the underlying matric...

On inconsistency of a pairwise comparison matrix

Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods

We consider the problems of estimating the parameters as well as the structure of binary-valued Markov networks. For maximizing the penalized log-likelihood, we implement an approximate procedure based on the pseudo-likelihood of Besag (1975) and generalize it to a fast exact algorithm. The exact algorithm starts with the pseudo-likelihood solution and then adjusts the pseudo-likelihood criteri...

GenericDiff: A General Framework for Model Comparison

This paper presents GenericDiff, a general framework for model comparison. The main design challenge of GenericDiff lies in how to strike a balance between being domain independent yet aware of domain-specific model properties and syntax. GenericDiff tackles this challenge by separating the specification of domainspecific inputs from the generic graph matching process and by making use of two d...