Agenda

Speaker: Michele Peruzzi, Duke University

The seminar will be done remotely via zoom, but will also be broadcast in the meeting room B in the Zeta building for those who would wish to attend in person.
Link Zoom https://unive.zoom.us/j/85153268624
Meeting ID: 851 5326 8624
Passcode: SanMarco2

Abstract:
Community ecologists seek to model the local abundance of multiple animal species while taking into account that observed counts only represent a portion of the underlying population size. Analogously, modeling spatial correlations in species' latent abundances is important when attempting to explain how species compete for scarce resources. We develop a Bayesian multi-species N-mixture model with spatial latent effects to address both issues. On one hand, our model accounts for imperfect detection by modeling local abundance via a Poisson log-linear model. Conditional on the local abundance, the observed counts have a binomial distribution. On the other hand, we let a directed acyclic graph restrict spatial dependence in order to speed up computations and use recently developed gradient-based Markov-chain Monte Carlo methods to sample a posteriori in the multivariate non-Gaussian data scenarios in which we are interested.

Bio Sketch:
Michele Peruzzi is currently a Postdoctoral Associate at Duke University, he holds a PhD in Statistics from Bocconi University. HIs research interests are mostly in the area of Bayesian regression with applications in large scale geostatistics and multivariate data.