RSS
- You are here:
- Research Repository UCD
- >
- College of Science
- >
- School of Mathematics and Statistics
- >
- Search
JavaScript is disabled for your browser. Some features of this site may not work without it.
Search
Search Results for Collection: School of Mathematics and Statistics
-
(Springer, 2010)This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, ...
-
(Journal of Machine Learning Research (JMLR), 2010)The p* model is widely used in social network analysis. The likelihood of a network under this model is impossible to calculate for all but trivially small networks. Various approximation have been presented in the literature, ...
-
(Springer, 2014)Monte Carlo algorithms often aim to draw from a distribution π by simulating a Markov chain with transition kernel P such that π is invariant under P. However, there are many situations for which it is impractical or ...
-
(Elsevier, 2015-02-03)Probabilistic K-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification ...
-
(Foundation for Open Access Statistics, 2014-10-24)In this paper we describe the main features of the Bergm package for the open-source Rsoftware which provides a comprehensive framework for Bayesian analysis of exponentialrandom graph models: tools for parameter estimation, ...
-
(International Society for Bayesian Analysis (ISBA), 2015)Many popular statistical models for complex phenomena areintractable, in the sense that the likelihood function cannot easily be evaluated.Bayesian estimation in this setting remains challenging, with a lack of ...
-
(Elsevier, 2016-07)We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo ...
-
(Elsevier, 2014-04)The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of this algorithm is proposed. The algorithm ...
-
(Cambridge University Press, 2016-12-12)We derive properties of Latent Variable Models for networks, a broad class ofmodels that includes the widely-used Latent Position Models. These include theaverage degree distribution, clustering coefficient, average path ...
-
(Cambridge University Press, 2017)We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this ...