71 citations · 110 across the 48 of their papers we have counts for
16 papers · 1 filter
Empowering Bayes with sources of uncertainty: from confidence modelling to sparse estimation
Rafael Mouallem Rosa, Julyan Arbel, Hien Duy Nguyen
We introduce sources of uncertainty (SoU) inference, a general framework that extends Bayesian inference by allowing researchers to explicitly encode heterogeneous confidence acros…
Shifted asymmetric Laplace mixtures of experts
Sphiwe B. Skhosana, Hien Duy Nguyen
Mixtures of experts (MoE) models provide a flexible framework for modelling heterogeneity in data for regression and model-based clustering and classification. MoE models for regre…
A variational framework for modal estimation
Tâm LeMinh, Julyan Arbel, Florence Forbes +1
Multivariate mode estimation arises in many statistical problems such as inverse problems, multimodal sampling, and density-based clustering, but becomes challenging in moderate to…
Finite sample inference for empirical Bayesian methods
Hien D Nguyen, Mayetri Gupta
In recent years, empirical Bayesian (EB) inference has become an attractive approach for estimation in parametric models arising in a variety of real-life problems, especially in c…
Universal inference with composite likelihoods
Hien D Nguyen, Jessica Bagnall-Guerreiro, Andrew T Jones
Maximum composite likelihood estimation is a useful alternative to maximum likelihood estimation when data arise from data generating processes (DGPs) that do not admit tractable j…
Order selection with confidence for finite mixture models
Hien D Nguyen, Daniel Fryer, Geoffrey McLachlan
The determination of the number of mixture components (the order) of a finite mixture model has been an enduring problem in statistical inference. We prove that the closed testing…
A binary-response regression model based on support vector machines
Hien D Nguyen, Daniel V Fryer
The soft-margin support vector machine (SVM) is a ubiquitous tool for prediction of binary-response data. However, the SVM is characterized entirely via a numerical optimization pr…
Approximate Bayesian computation via the energy statistic
Hien D. Nguyen, Julyan Arbel, Hongliang Lü +1
Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely u…
False discovery rate control under reduced precision computation for analysis of neuroimaging data
Hien D. Nguyen, Yohan Yee, Geoffrey J. McLachlan +1
The mitigation of false positives is an important issue when conducting multiple hypothesis testing. The most popular paradigm for false positives mitigation in high-dimensional ap…
Approximation results regarding the multiple-output mixture of linear experts model
Hien D. Nguyen, Faicel Chamroukhi, Florence Forbes
Mixture of experts (MoE) models are a class of artificial neural networks that can be used for functional approximation and probabilistic modeling. An important class of MoE models…
Some Theoretical Results Regarding the Polygonal Distribution
Hien D Nguyen, Geoffrey J McLachlan
The polygonal distributions are a class of distributions that can be defined via the mixture of triangular distributions over the unit interval. The class includes the uniform and…
Near Universal Consistency of the Maximum Pseudolikelihood Estimator for Discrete Models
Hien D. Nguyen
Maximum pseudolikelihood (MPL) estimators are useful alternatives to maximum likelihood (ML) estimators when likelihood functions are more difficult to manipulate than their margin…
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling
Hien D. Nguyen, Jeremy F. P. Ullmann, Geoffrey J. McLachlan +5
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescen…
Linear Mixed Models with Marginally Symmetric Nonparametric Random Effects
Hien D. Nguyen, Geoffrey J. McLachlan
Linear mixed models (LMMs) are used as an important tool in the data analysis of repeated measures and longitudinal studies. The most common form of LMMs utilize a normal distribut…
Spatial Clustering of Time-Series via Mixture of Autoregressions Models and Markov Random Fields
Hien D Nguyen, Geoffrey J McLachlan, Jeremy F P Ullmann +1
Time-series data arise in many medical and biological imaging scenarios. In such images, a time-series is obtained at each of a large number of spatially-dependent data units. It i…
Mixtures of Spatial Spline Regressions
Hien D. Nguyen, Geoffrey J. McLachlan, Ian A. Wood
We present an extension of the functional data analysis framework for univariate functions to the analysis of surfaces: functions of two variables. The spatial spline regression (S…