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DIRECTIONS IN DATA SCIENCE: Francesco Bartolucci 15 March 2016 at 12:30 Room A14

Composite likelihood inference for hidden Markov models for dynamic networks

Francesco Bartolucci

Professor of Statistics
Dipartimento di Economia
Universitą di Perugia

  • 15 March 2016
  • 12:30 - 13:30
  • Room A14

 

In this scientific seminar, Bartolucci introduces a hidden Markov model for dynamic network data where directed relations among a set of units are observed at different time occasions. The model can also be used with minor adjustments to deal with undirected networks. In the directional case, dyads referred to each pair of units are explicitly modelled conditional on the latent states of both units. Given the complexity of the model, we propose a composite likelihood method for making inference on its parameters. This method is studied in detail for the directional case by a simulation study in which different scenarios are considered.
The proposed approach is illustrated by an example based on the well-known Enron dataset about email exchange.

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