73 citations · 232 across the 28 of their papers we have counts for
33 papers
Bayesian Poisson Regression and Tensor Train Decomposition Model for Learning Mortality Pattern Changes during COVID-19 Pandemic
Wei Zhang, Antonietta Mira, Ernst C. Wit
COVID-19 has led to excess deaths around the world, however it remains unclear how the mortality of other causes of death has changed during the pandemic. Aiming at understanding t…
Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies
Francesco Denti, Stefano Peluso, Michele Guindani +1
The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly m…
A predictive model for planning emergency events rescue during COVID-19 in Lombardy, Italy
Angela Andreella, Antonietta Mira, Spyros Balafas +5
Italy, particularly the Lombardy region, was among the first countries outside of Asia to report cases of COVID-19. The emergency medical service called Regional Emergency Agency (…
On the intrinsic dimensionality of Covid-19 data: a global perspective
Abhishek Varghese, Edgar Santos-Fernandez, Francesco Denti +2
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covi…
Learning Summary Statistics for Bayesian Inference with Autoencoders
Carlo Albert, Simone Ulzega, Firat Ozdemir +2
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of obser…
A Bayesian Semiparametric Vector Multiplicative Error Model
Nicola Donelli, Stefano Peluso, Antonietta Mira
Interactions among multiple time series of positive random variables are crucial in diverse financial applications, from spillover effects to volatility interdependence. A popular…
Distributional Results for Model-Based Intrinsic Dimension Estimators
Francesco Denti, Diego Doimo, Alessandro Laio +1
Modern datasets are characterized by a large number of features that may conceal complex dependency structures. To deal with this type of data, dimensionality reduction techniques…
A Bayesian latent allocation model for clustering compositional data with application to the Great Barrier Reef
Luiza Piancastelli, Nial Friel, Julie Vercelloni +2
Relative abundance is a common metric to estimate the composition of species in ecological surveys reflecting patterns of commonness and rarity of biological assemblages. Measureme…
Compatibility-based Bayesian inference: asymptotic limits, variational principles, and Wasserstein geometry
Daniele Cassani, Antonietta Mira, Marco Tarsia
Approximate Bayesian Computation (ABC) replaces intractable likelihood evaluations with comparisons between observed and simulated data. We develop a measure-theoretic formulation…
Revealing three-dimensional quantum criticality by Sr-substitution in Han Purple
Stephan Allenspach, Pascal Puphal, Joosep Link +20
Classical and quantum phase transitions (QPTs), with their accompanying concepts of criticality and universality, are a cornerstone of statistical thermodynamics. An exemplary cont…
Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries
Louis Raynal, Sixing Chen, Antonietta Mira +1
Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires t…
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Ritabrata Dutta, Karim Zouaoui-Boudjeltia, Christos Kotsalos +6
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffec…
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
Francesco Denti, Federico Camerlenghi, Michele Guindani +1
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In p…
The role of intrinsic dimension in high-resolution player tracking data -- Insights in basketball
Edgar Santos-Fernandez, Francesco Denti, Kerrie Mengersen +1
A new range of statistical analysis has emerged in sports after the introduction of the high-resolution player tracking technology, specifically in basketball. However, this high d…
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
Abhishek Varghese, Christopher Drovandi, Kerrie Mengersen +1
The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to…
Conditionally Gaussian Random Sequences for an Integrated Variance Estimator with Correlation between Noise and Returns
Stefano Peluso, Antonietta Mira, Pietro Muliere
Correlation between microstructure noise and latent financial logarithmic returns is an empirically relevant phenomenon with sound theoretical justification. With few notable excep…
Data segmentation based on the local intrinsic dimension
Michele Allegra, Elena Facco, Francesco Denti +2
One of the founding paradigms of machine learning is that a small number of variables is often sufficient to describe high-dimensional data. The minimum number of variables require…
Regularized Zero-Variance Control Variates
Leah F. South, Chris J. Oates, Antonietta Mira +1
Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once t…
Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks
Francesco Bartolucci, Antonietta Mira, Stefano Peluso
A new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of…
Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water
Ritabrata Dutta, Zacharias Faidon Brotzakis, Antonietta Mira
Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties given an ergodic sampling and an accurate force-field. The force-field parameters a…
Likelihood-free parameter estimation for dynamic queueing networks: case study of passenger flow in an international airport terminal
Anthony Ebert, Ritabrata Dutta, Kerrie Mengersen +3
Dynamic queueing networks (DQN) model queueing systems where demand varies strongly with time, such as airport terminals. With rapidly rising global air passenger traffic placing i…
Flexible model selection for mechanistic network models
Sixing Chen, Antonietta Mira, Jukka-Pekka Onnela
Network models are applied across many domains where data can be represented as a network. Two prominent paradigms for modeling networks are statistical models (probabilistic model…
Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data
Maksym Byshkin, Alex Stivala, Antonietta Mira +2
A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural propertie…
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi +5
ABCpy is a highly modular scientific library for Approximate Bayesian Computation (ABC) written in Python. The main contribution of this paper is to document a software engineering…