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Found 7 papers, 1 papers with code
Analyzing social media with crowdsourcing in Crowd4SDG
However, finding relevant information among millions of posts being posted every day can be difficult, and developing a data analysis project usually requires time and technical skills.
A first approach to closeness distributions
Probabilistic graphical models allow us to encode a large probability distribution as a composition of smaller ones.
Parametrization invariant interpretation of priors and posteriors
In this paper we leverage on probability over Riemannian manifolds to rethink the interpretation of priors and posteriors in Bayesian inference.
Dual Stochastic Natural Gradient Descent and convergence of interior half-space gradient approximations
Here, we propose a stochastic optimization method for MLR based on manifold optimization concepts which (i) has per-iteration computational complexity is linear in the number of parameters and (ii) can be proven to converge.
Sufficient and necessary conditions for Dynamic Programming in Valuation-Based Systems
In spite of that, the mainline of Pouly and Kohlas' theory is correct, although some of the necessary conditions have to be revised.
Even more generic solution construction in Valuation-Based Systems
Valuation algebras abstract a large number of formalisms for automated reasoning and enable the definition of generic inference procedures.
Bayesian Conditional Gaussian Network Classifiers with Applications to Mass Spectra Classification
Classifiers based on probabilistic graphical models are very effective.
