39 citations · 140 across the 29 of their papers we have counts for
16 papers · 1 filter
Bayesian Posterior Learning of Mixed Graphical Models
Erdong Guo, Alexandros Beskos, Maria De Iorio
Mixed Graphical Models (MGMs) provide a flexible framework for structure learning from heterogeneous data by treating sets of both continuous and discrete. Bayesian inference for M…
Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
Luca Aiello, Raffaele Argiento, Alexandros Beskos +1
Recent research has led to the development of MCMC algorithms with likelihood-informed proposals when targeting posterior distributions supported on discrete state spaces. Our work…
Graph of Graphs: From Nodes to Supernodes in Graphical Models
Maria De Iorio, Willem van den Boom, Alexandros Beskos +2
High-dimensional data analysis typically focuses on low-dimensional structure, often to aid interpretation and computational efficiency. Graphical models provide a powerful methodo…
The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis
Willem van den Boom, Maria De Iorio, Fang Qian +1
Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival anal…
Conditional partial exchangeability: a probabilistic framework for multi-view clustering
Beatrice Franzolini, Maria De Iorio, Johan Eriksson
Standard clustering techniques assume a common configuration for all features in a dataset. However, when dealing with multi-view or longitudinal data, the clusters' number, freque…
Repulsion, Chaos and Equilibrium in Mixture Models
Andrea Cremaschi, Timothy M. Wertz, Maria De Iorio
Mixture models are commonly used in applications with heterogeneity and overdispersion in the population, as they allow the identification of subpopulations. In the Bayesian framew…
Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market
Beatrice Franzolini, Alexandros Beskos, Maria De Iorio +2
Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future inv…
Bayesian Dynamic Network Modelling: an application to metabolic associations in cardiovascular diseases
Marco Molinari, Andrea Cremaschi, Maria De Iorio +3
We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients.…
Bayesian clustering of multiple zero-inflated outcomes
Beatrice Franzolini, Andrea Cremaschi, Willem van den Boom +1
Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the Hurdle model, which explicitly models the pro…
On a wider class of prior distributions for graphical models
Abhinav Natarajan, Willem van den Boom, Kristoforus Bryant Odang +1
Gaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph stru…
Bayesian Learning of Graph Substructures
Willem van den Boom, Maria De Iorio, Alexandros Beskos
Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges…
Joint modelling of association networks and longitudinal biomarkers: an application to child obesity
Andrea Cremaschi, Maria De Iorio, Narasimhan Kothandaraman +3
The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importanc…
Cohesion and Repulsion in Bayesian Distance Clustering
Abhinav Natarajan, Maria De Iorio, Andreas Heinecke +2
Clustering in high-dimensions poses many statistical challenges. While traditional distance-based clustering methods are computationally feasible, they lack probabilistic interpret…
Bayesian inference on the number of recurrent events: A joint model of recurrence and survival
Willem van den Boom, Maria De Iorio, Marta Tallarita
The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual's process of rehospitalizations and the number of rehos…
Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
Linda S. L. Tan, Ajay Jasra, Maria De Iorio +1
We investigate the effect of cadmium (a toxic environmental pollutant) on the correlation structure of a number of urinary metabolites using Gaussian graphical models (GGMs). The i…
A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures
William Astle, Maria De Iorio, Sylvia Richardson +2
Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods for estimating…