Toolbox for Mixed Vine Copulas Now Available

The Mixed Vine Copula Toolbox for Matlab is now available in the Code Section. In this toolbox, we implemented a complete framework based on canonical vine copulas for modelling multivariate data that are partly discrete and partly continuous. The resulting multivariate distributions are flexible with rich dependence structures and arbitrary margins. For continuous margins, we provide implementations of the normal and the gamma distributions. For discrete margins, we provide the Poisson, binomial and negative binomial distributions. As bivariate copula building blocks, we provide the Gaussian, student and Clayton families as well as rotation transformed Clayton families. The toolbox includes methods for sampling, likelihood calculation and inference, all of which have quadratic complexity. These procedures are combined to estimate entropy and mutual information by means of Monte Carlo integration.

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Brain functions likely emerge from the concerted, context-dependent operations of its microscopic and macroscopic networks. Therefore, the organization and operational principles of such complex systems may be best investigated by using multi-modal approaches, including concurrent measurements of neural activity on multiple spatiotemporal scales. Performing and interpreting such multi-scale measures, though, presents enormous challenges for both experimental and mathematical neuroscientists. Existing analysis methods, however, make limited use of newly acquired concurrent multi-scale information.

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