39 citations · 140 across the 29 of their papers we have counts for
29 papers
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…
From Past to Future: Digital Methods Towards Artefact Analysis
Andrew Harris, Andrea Cremaschi, Tse Siang Lim +2
Over the past two decades, Digital Humanities has transformed the landscape of humanities and social sciences, enabling advanced computational analysis and interpretation of extens…
Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia
Andrea Cremaschi, Wenjian Yang, Maria De Iorio +3
Acute lymphoblastic leukemia (ALL) is a heterogeneous hematologic malignancy involving the abnormal proliferation of immature lymphocytes, accounting for most pediatric cancer case…
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…
Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks
Erdong Guo, David Draper, Maria De Iorio
Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical…
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…
The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
Willem van den Boom, Alexandros Beskos, Maria De Iorio
Gaussian graphical models can capture complex dependency structures among variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate…
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…
Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo
Willem van den Boom, Ajay Jasra, Maria De Iorio +2
Markov chain Monte Carlo (MCMC) is a powerful methodology for the approximation of posterior distributions. However, the iterative nature of MCMC does not naturally facilitate its…
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…
Dynamic degree-corrected blockmodels for social networks: a nonparametric approach
Linda S. L. Tan, Maria De Iorio
A nonparametric approach to the modeling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmo…
Bayesian Nonparametric Modelling of Joint Gap Time Distributions for Recurrent Event Data
Marta Tallarita, Maria De Iorio, Alessandra Guglielmi +1
We propose autoregressive Bayesian semi-parametric models for waiting times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariat…
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 comparative review of variable selection techniques for covariate dependent Dirichlet process mixture models
William Barcella, Maria De Iorio, Gianluca Baio
Dirichlet Process Mixture (DPM) models have been increasingly employed to specify random partition models that take into account possible patterns within the covariates. Furthermor…
Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection
William Barcella, Maria De Iorio, Gianluca Baio +1
Lower urinary tract symptoms (LUTS) can indicate the presence of urinary tract infection (UTI), a condition that if it becomes chronic requires expensive and time consuming care as…