Museos, redes sociales y tecnología 2.0

Índice: - Sobre museos, redes sociales y tecnología 2.0 (Alex Ibáñez Etxeberria). - Sitios web y museos: nuevas aplicaciones para el aprendizaje informal (Mikel Asensio, Elena Asenjo y Alex Ibáñez Etxeberria). - From headphones to microphones: mobile social media in the museum as distributed network (Nancy Proctor). - Mobile learning y patrimionio: aprendiendo historia con mi teléfono, mi GPS y mi PDA (Alex Ibáñez Etxeberria, Mikel Asensio y José Miguel Correa). - Digital asset management strategies for multi-platform content delivery (Titus Bicknell). - Redes sociales y museos participativos: la irrupción de las tecnologías 2.0 en la sociedad y su aplicación en los museos a través del caso de Arazi (Juan José Aranburu).

Kalman Filtering in R

Support in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in R to help the prospective user to make an informed choice.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

New climate scenario framework implementation in the GCAM integrated assessment model

48 p.