Browsing by Author "Friel, Nial"

Bayesian exponential random graph models with nodal random effects

Thiemichen, S. ; Friel, Nial ; Caimo, Alberto ; Kauermann, G. (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 ...

Bayesian model selection for the latent position cluster model for Social Networks

Friel, Nial ; Ryan, Catriona ; Wyse, Jason (Cambridge University Press, 2017-03)

The latent position cluster model is a popular model for the statistical analysis of network data. This model assumes that there is an underlying latent space in which the actors follow a finite mixture distribution. ...

Bergm: Bayesian Exponential Random Graphs in R

Caimo, Alberto ; Friel, Nial (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, ...

Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

Friel, Nial ; Stoer, Julien (Microtome Publishing, 2015-05-12)

Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods offer a principled means to construct ...

Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion

Bertoletti, Marco ; Friel, Nial ; Rastelli, Riccardo (Springer, 2015-08)

The integrated completed likelihood (ICL) criterion has proven to be a very popular approach in model-based clustering through automatically choosing the number of clusters in a mixture model. This approach effectively ...

Classification using distance nearest neighbours

Friel, Nial ; Pettitt, Anthony (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, ...

Deterministic Bayesian inference for the p* model

Austad, Haakon ; Friel, Nial (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, ...

Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution

Bouranis, Lampros ; Friel, Nial ; Maire, Florian (Elsevier, 2017-07)

Exponential random graph models are an important tool in the statistical analysis of data. However, Bayesian parameter estimation for these models is extremely challenging, since evaluation of the posterior distribution ...

Efficient model selection for probabilistic K nearest neighbour classification

Won Yoon, Ji ; Friel, Nial (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 ...

Evaluating Squat Performance with a Single Inertial Measurement Unit

O'Reilly, Martin ; Whelan, Daragh ; Chanialidis, Charalampos ; Friel, Nial ; Delahunt, Eamonn ; Ward, Tomás ; Caulfield, Brian (IEEE, 2015-06-12)

Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate ...

Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods

Friel, Nial ; Mira, Antonietta ; Oates, Chris J. (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 ...

A generalized multiple-try version of the Reversible Jump algorithm

Pandolfi, Slivia ; Bartolucci, Francesco ; Friel, Nial (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 ...

Inferring structure in bipartite networks using the latent block model and exact ICL

Wyse, Jason ; Friel, Nial ; Latouche, Pierre (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 ...

Investigation of the widely applicable Bayesian information criterion

Friel, Nial ; McKeone, J. P. ; Oates, Chris J. ; Pettitt, Anthony (Springer, 2017-05)

The widely applicable Bayesian information criterion (WBIC) is a simple and fast approximation to the model evidence that has received little practical consideration. WBIC uses the fact that the log evidence can be written ...

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Alquier, Pierre ; Friel, Nial ; Everitt, R. G. ; Boland, Aidan (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 ...

Properties of Latent Variable Network Models

Rastelli, Riccardo ; Friel, Nial ; Raftery, Adrian E. (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 ...

Browsing by Author "Friel, Nial"

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