91 citations · 179 across the 14 of their papers we have counts for
15 papers
Bayesian Cluster Weighted Gaussian Models
Panagiotis Papastamoulis, Konstantinos Perrakis
We introduce a novel class of Bayesian mixtures for normal linear regression models which incorporates a further Gaussian random component for the distribution of the predictor var…
bayesCureRateModel: Bayesian Cure Rate Modeling for Time to Event Data in R
Panagiotis Papastamoulis, Fotios Milienos
The family of cure models provides a unique opportunity to simultaneously model both the proportion of cured subjects (those not facing the event of interest) and the distribution…
Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution
Michail Tsagris, Panagiotis Papastamoulis, Shogo Kato
In 2020, two novel distributions for the analysis of directional data were introduced: the spherical Cauchy distribution and the Poisson kernel-based distribution. This paper provi…
Multinomial mixture for spatial data
Anna Nalpantidi, Dimitris Karlis, Panagiotis Papastamoulis
The purpose of this paper is to extend standard finite mixture models in the context of multinomial mixtures for spatial data, in order to cluster geographical units according to d…
Bayesian inference and cure rate modeling for event history data
Panagiotis Papastamoulis, Fotios Milienos
Estimating model parameters of a general family of cure models is always a challenging task mainly due to flatness and multimodality of the likelihood function. In this work, we pr…
Model based clustering of multinomial count data
Panagiotis Papastamoulis
We consider the problem of inferring an unknown number of clusters in replicated multinomial data. Under a model based clustering point of view, this task can be treated by estimat…
On the identifiability of Bayesian factor analytic models
Panagiotis Papastamoulis, Ioannis Ntzoufras
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup…
Clustering Multivariate Data using Factor Analytic Bayesian Mixtures with an Unknown Number of Components
Panagiotis Papastamoulis
Recent work on overfitting Bayesian mixtures of distributions offers a powerful framework for clustering multivariate data using a latent Gaussian model which resembles the factor…
Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling
Panagiotis Papastamoulis, Takanori Furukawa, Norman van Rhijn +3
We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. Th…
Overfitting Bayesian Mixtures of Factor Analyzers with an Unknown Number of Components
Panagiotis Papastamoulis
Recent advances on overfitting Bayesian mixture models provide a solid and straightforward approach for inferring the underlying number of clusters and model parameters in heteroge…
Bayesian estimation of Differential Transcript Usage from RNA-seq data
Panagiotis Papastamoulis, Magnus Rattray
Next generation sequencing allows the identification of genes consisting of differentially expressed transcripts, a term which usually refers to changes in the overall expression l…
BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data
Panagiotis Papastamoulis, Magnus Rattray
The BayesBinMix package offers a Bayesian framework for clustering binary data with or without missing values by fitting mixtures of multivariate Bernoulli distributions with an un…
label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs
Panagiotis Papastamoulis
Label switching is a well-known and fundamental problem in Bayesian estimation of mixture or hidden Markov models. In case that the prior distribution of the model parameters is th…
Fast and accurate approximate inference of transcript expression from RNA-seq data
James Hensman, Panagiotis Papastamoulis, Peter Glaus +2
Motivation: Assigning RNA-seq reads to their transcript of origin is a fundamental task in transcript expression estimation. Where ambiguities in assignments exist due to transcrip…
A Bayesian model selection approach for identifying differentially expressed transcripts from RNA-Seq data
Panagiotis Papastamoulis, Magnus Rattray
Recent advances in molecular biology allow the quantification of the transcriptome and scoring transcripts as differentially or equally expressed between two biological conditions.…