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Profil bibliographique

Nicola Bulso

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

10Publications signalées
77Citations signalées
2Affiliations récentes

Les institutions déclarées

Les domaines associés

Statistical Methods and InferenceMeta-analysis and systematic reviewsAdvanced Statistical Methods and ModelsStatistical Mechanics and EntropySustainability and Climate Change Governance

Les publications récentes

Accès ouvert 2022 article OpenAlex

Insights into the quantification and reporting of model-related uncertainty across different disciplines

Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim et autres

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and …

no, gb, us, be, es, de, ch, ca (code pays fourni par la source)

34 citations iScience
Accès ouvert 2022 dataset OpenAlex

Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines

Emily G. Simmonds, Kwaku Adjei Peprah, Christoffer Wold Andersen, Janne Cathrin Helte Aspheim et autres

This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines. Description of the data …

no, gb, us, be, es, de, ch, ca, au (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)
Accès ouvert 2022 dataset OpenAlex

Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines

Emily G. Simmonds, Kwaku Adjei Peprah, Christoffer Wold Andersen, Janne Cathrin Helte Aspheim et autres

This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines. Description of the data …

no, gb, us, be, es, de, ch, ca, au (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)
Accès ouvert 2022 preprint OpenAlex

How is model-related uncertainty quantified and reported in different disciplines?

Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen, Janne Cathrin Hetle Aspheim et autres

How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative science now highly influential in the public sphere and the results …

no, us, gb, sk, de, ch, ca (code pays fourni par la source)

1 citation arXiv (Cornell University)
Accès ouvert 2021 article OpenAlex

Restricted Boltzmann Machines as Models of Interacting Variables

Nicola Bulso, Yasser Roudi

We study the type of distributions that restricted Boltzmann machines (RBMs) with different activation functions can express by investigating the effect of the activation function of the hidden nodes on the marginal distribution they impose on observed binary nodes. We report an …

it, no (code pays fourni par la source)

0 citations Neural Computation
2019 article OpenAlex

On the Complexity of Logistic Regression Models

Nicola Bulso, Matteo Marsili, Yasser Roudi

We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997 ). We find that the complexity of logistic models with binary inputs depends not only on the …

no, it (code pays fourni par la source)

26 citations Neural Computation
Accès ouvert 2019 preprint OpenAlex

On the complexity of logistic regression models

Nicola Bulso, Matteo Marsili, Yasser Roudi

We investigate the complexity of logistic regression models which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997). We find that the complexity of logistic models with binary inputs does not only depend on the …

no, it (code pays fourni par la source)

0 citations arXiv (Cornell University)
Accès ouvert 2016 article OpenAlex

Sparse model selection in the highly under-sampled regime

Nicola Bulso, Matteo Marsili, Yasser Roudi

We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and …

it, us (code pays fourni par la source)

10 citations Journal of Statistical Mechanics Theory and Experiment
Accès ouvert 2014 article OpenAlex

Effective dissipation and nonlocality induced by nonparaxiality

Nicola Bulso, Claudio Conti

We investigate beam diffraction and spatial modulation instability of coherent light beams propagating in the nonparaxial regime in a nonlinear Kerr medium. We study the instability of plane-wave solutions in terms of the degree of nonparaxiality, beyond the regime of validity of …

it (code pays fourni par la source)

6 citations Physical Review A

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