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Gianluca Mastrantonio
Researcher at Polytechnic University of Turin
Publications - 54
Citations - 541
Gianluca Mastrantonio is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Hidden Markov model & Gaussian process. The author has an hindex of 11, co-authored 48 publications receiving 453 citations. Previous affiliations of Gianluca Mastrantonio include Roma Tre University & Sapienza University of Rome.
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The seascape of demersal fish nursery areas in the North Mediterranean Sea, a first step towards the implementation of spatial planning for trawl fisheries.
Francesco Colloca,Germana Garofalo,Isabella Bitetto,Maria Teresa Facchini,Fabio Grati,Angela Martiradonna,Gianluca Mastrantonio,Nikolaos Nikolioudakis,Francesc Ordinas,Giuseppe Scarcella,George Tserpes,M. Pilar Tugores,Vasilis D. Valavanis,Roberto Carlucci,Fabio Fiorentino,Maria Cristina Follesa,M. Iglesias,Leyla Knittweis,E. Lefkaditou,Giuseppe Lembo,Chiara Manfredi,Enric Massutí,Marie Louise Pace,Nadia Papadopoulou,Paolo Sartor,Caleb Smith,Maria Teresa Spedicato +26 more
TL;DR: The new knowledge on the distribution and persistence of demersal nurseries provided in this study can support the application of spatial conservation measures, such as the designation of no-take Marine Protected Areas in EU Mediterranean waters and their inclusion in a conservation network.
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Counts of unique females with cubs in the Apennine brown bear population, 2006–2014
TL;DR: In this article, the authors conducted surveys of females with cubs (FWC) to estimate the minimum number of female bears that reproduced and annual productivity in this bear population and discriminated unique family groups based on simultaneity of sightings and presence of individually recognizable bears.
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Bayesian Hidden Markov Modelling Using Circular-Linear General Projected Normal Distribution
TL;DR: In this article, a multivariate hidden Markov model is proposed to jointly cluster time-series observations with different support, i.e., circular and linear, which allows for bimodal and skewed cluster-specific distributions for the circular variable.
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Discussing the “big n problem”
TL;DR: Two approaches, respectively based on stochastic partial differential equations and integrated nested Laplace approximation, and on the tapering of the spatial covariance matrix are focused on.
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Bayesian hidden Markov modelling using circular‐linear general projected normal distribution
TL;DR: In this article, a multivariate hidden Markov model is proposed to jointly cluster time-series observations with different support, that is, circular and linear, which allows for bimodal and/or skewed cluster-specific distributions for the circular variable.