29 citations · 82 across the 19 of their papers we have counts for
20 papers
Integrating Deep Learning and Spatial Statistics in Marine Ecosystem Monitoring
Gian Mario Sangiovanni, Gianluca Mastrantonio, Daniele Ventura +2
In ecology, photogrammetry is a crucial method for efficiently collecting non-destructive samples of natural environments. When estimating the spatial distribution of animals, dete…
Modelling benthic animals in space and time using Bayesian Point Process with cross validation: the case of Holoturians
Daniele Poggio, Gian Mario Sangiovanni, Gianluca Mastrantonio +4
Understanding the spatial distribution of Holothurians is an essential task for ecosystem monitoring and sustainable management, particularly in the Mediterranean habitats. However…
BayVel: A Bayesian Framework for RNA Velocity Estimation in Single-Cell Transcriptomics
Elena Sabbioni, Enrico Bibbona, Gianluca Mastrantonio +1
RNA velocity is a model of gene expression dynamics designed to analyze single-cell RNA sequencing (scRNA-seq) data, and it has recently gained significant attention. However, desp…
Bayesian inference of Latent Spectral Shapes
Hiu Ching Yip, Daria Valente, Enrico Bibbona +3
This paper proposes a hierarchical spatial-temporal model for modelling the spectrograms of animal calls. The motivation stems from analyzing recordings of the so-called grunt call…
Bayesian size-and-shape regression modelling
Antonio Di Noia, Gianluca Mastrantonio, Giovanna Jona Lasinio
Building on Dryden et al. (2021), this note presents the Bayesian estimation of a regression model for size-and-shape response variables with Gaussian landmarks. Our proposal fits…
Estimating the optimal time to perform a PET-PSMA exam in prostatectomized patients based on data from clinical practice
Martina Amongero, Gianluca Mastrantonio, Stefano De Luca +1
Prostatectomized patients are at risk of resurgence, and for this reason, during a follow-up period, they are monitored for Prostate Specific Antigen (PSA) growth, an indicator of…
The modeling of multiple animals that share behavioral features
Gianluca Mastrantonio
In this work, we propose a model that can be used to infer the behavior of multiple animals. Our proposal is defined as a set of hidden Markov models that are based on the sticky h…
Modeling animal movement with directional persistence and attractive points
Gianluca Mastrantonio
GPS technology is currently easily accessible to researchers, and many animal movement datasets are available. Two of the main features that a model which describes an animal's pat…
CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data
Giovanna Jona Lasinio, Mario Santoro, Gianluca Mastrantonio
CircSpaceTime is the only R package currently available that implements Bayesian models for spatial and spatio-temporal interpolation of circular data. Such data are often found in…
New formulation of the Logistic-Gaussian process to analyze trajectory tracking data
Gianluca Mastrantonio, Clara Grazian, Sara Mancinelli +1
Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data. These data are of…
Semi-parametric Bayesian change-point model based on the Dirichlet process
Gianluca Mastrantonio
In this work we introduce a semi-parametric Bayesian change-point model, defining its time dynamic as a latent Markov process based on the Dirichlet process. We treat the number of…
A Hierarchical Multivariate Spatio-Temporal Model for Large Clustered Climate data with Annual Cycles
Gianluca Mastrantonio, Giovanna Jona Lasinio, Alessio Pollice +4
We present a multivariate hierarchical space-time model to describe the joint series of monthly extreme temperatures and amounts of rainfall. Data are available for 360 monitoring…
The joint projected normal and skew-normal: a distribution for poly-cylindrical data
Gianluca Mastrantonio
The contribution of this work is the introduction of a multivariate circular-linear (or poly- cylindrical) distribution obtained by combining the projected and the skew-normal. We…
Hidden Markov model for discrete circular-linear wind data time series
Gianluca Mastrantonio, Gianfranco Calise
In this work, we deal with a bivariate time series of wind speed and direction. Our observed data have peculiar features, such as informative missing values, non-reliable measures…
The wrapped skew Gaussian process for analyzing spatio-temporal data
Gianluca Mastrantonio, Giovanna Jona Lasinio, Alan E. Gelfand
We consider modeling of angular or directional data viewed as a linear variable wrapped onto a unit circle. In particular, we focus on the spatio-temporal context, motivated by a c…
Spatio-temporal circular models with non-separable covariance structure
Gianluca Mastrantonio, Giovanna Jona Lasinio, Alan E. Gelfand
Circular data arise in many areas of application. Recently, there has been interest in looking at circular data collected separately over time and over space. Here, we extend some…
Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular-linear data
Gianluca Mastrantonio, Alessio Pollice, Francesca Fedele
Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy).…
The Joint Projected and Skew Normal
Gianluca Mastrantonio
We introduce a new multivariate circular linear distribution suitable for modeling direction and speed in (multiple) animal movement data. To properly account for specific data fea…
On initial direction, orientation and discreteness in the analysis of circular variables
Gianluca Mastrantonio, Giovanna Jona Lasinio, Antonello Maruotti +1
In this paper, we propose a discrete circular distribution obtained by extending the wrapped Poisson distribution. This new distribution, the Invariant Wrapped Poisson (IWP), enjoy…
Bayesian Hidden Markov Modelling Using Circular-Linear General Projected Normal Distribution
Gianluca Mastrantonio, Antonello Maruotti, Giovanna Jona Lasinio
We introduce a multivariate hidden Markov model to jointly cluster time-series observations with different support, i.e. circular and linear. Relying on the general projected norma…