1,721,951 research outputs found
Premessa [di Davide Mastrantonio ed Eugenio Salvatore]
No full textIn questa premessa viene presentato il volume e viene commentato il primo articolo, che costituisce uno scritto postumo di Luca Serianni
Struttura e novità del Sillabo CFSIE
No full textIl capitolo descrive in dettaglio la struttura del sillabo e ne mette in luce le novità rispetto ai lavori analoghi precedenti
Parte 2: competenze grammaticali
No full textLa parte 2 contiene la descrizione delle competenze grammaticali graduata per livelli del Quadro Comune Europeo di Riferimento dall'A1 al C2
CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data
Full text linkCircSpaceTime 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 applications where, among the many, wind directions, animal movement directions, and wave directions are involved. To analyse such data, we need models for observations at locations s and times t, as the so-called geostatistical models, providing structured dependence assumed to decay in distance and time. The approach we take begins with Gaussian processes defined for linear variables over space and time. Then, we use either wrapping or projection to obtain processes for circular data. The models are cast as hierarchical, with fitting and inference within a Bayesian framework. Altogether, this package implements work developed by a series of papers, by Jona Lasinio, Mastrantonio, Wang, and Gelfand. All procedures are written using Rcpp. Estimates are obtained by MCMC, allowing parallelized multiple chains run. The implementation of the proposed models is considerably improved on the simple routines adopted in the research papers. As original running examples, for the spatial and spatio-temporal settings, we use wind directions datasets over central Italy
Entrevista con los doctores en Bioquímica, María Elena Marson y Guido Mastrantonio, investigadores de la Planta Piloto Multipropósito – Laboratorio de Servicios a la Industria y al Sistema Científico
No full textEntrevista con losdoctores en Bioquímica, María Elena Marson y Guido Mastrantonio, investigadores de la Planta Piloto Multipropósito – Laboratorio de Servicios a la Industria y al Sistema Científico. Se habó del desarrollo de un método para que las madres afectadas por el Mal de Chagas puedan amamantar a sus hijos sin contagiarlos
Sul cloze mirato e semplificato nella didattica del registro accademico
Full text linkIl saggio presenta anzitutto lo stato di avanzamento delle ricerche sulla varietà definita "registro accademico". Secondariamente questa nozione è sviluppata nel terreno applicato della didattica del lessico, mostrando come si può costruire un cloze mirato e semplificato. L'articolo si chiude infine con l'analisi di alcuni casi provenienti da scritture studentesche e con considerazioni relative all'interlingua degli studenti
Pavese, la lingua, il dialetto
Full text linkIl linguaggio e lo stile di Pavese lasciano traccia sia dei condizionamenti del momento storico in cui si inseriscono (gli anni Trenta e Quaranta del Novecento), sia del paziente e tenace lavoro di sperimentazione formale compiuto dall'autore. In questo breve articolo si esplora la tensione tra classicità e storicità di Pavese, e si farà con gli strumenti dell'analisi linguistica
The joint projected normal and skew-normal: A distribution for poly-cylindrical data
Full text linkThis paper introduces a multivariate circular–linear (or poly-cylindrical) distribution obtained by combining the projected and the skew-normal. We show the flexibility of our proposal, its closure under marginalization, and how to quantify multivariate dependence. Due to a non-identifiability issue that our proposal inherits from the projected normal, a computational problem arises. We overcome it in a Bayesian framework, adding suitable latent variables and showing that posterior samples can be obtained with a post-processing of the estimation algorithm output. Under specific prior choices, this approach enables us to implement a Markov chain Monte Carlo algorithm relying only on Gibbs steps, where the updates of the parameters are done as if we were working with a multivariate normal likelihood. The proposed approach can also be used with the projected normal. As a proof of concept, on simulated examples we show the ability of our algorithm in recovering the parameter values and to solve the identification problem. Then the proposal is used in a real data example, where the turning-angles (circular variables) and the logarithm of the step-lengths (linear variables) of four zebras are modeled jointly
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