"Bayesian Nonparametric Modeling of Network Data"

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Event details

Date 19.01.2017
Hour 15:0016:00
Speaker Dr. Daniele Durante  (Università di Padova)
Location
Category Conferences - Seminars
Many fields of research provide increasingly complex data along with novel motivating applications and new methodological questions. In approaching these data sets it is fundamental to rely on parsimonious representations which make the problem tractable and provide interpretable inference procedures to draw meaningful conclusions. However, in reducing complexity, it is important to avoid restrictive models that lead to
inadequate characterization of relevant patterns underlying the observed data. Within this framework, network data representing relationship structures among a set of nodes are a relevant example. Although there has been abundant focus on models for a single network, there is a lack of methods for replicated network-valued data monitored in different times or collected from a common population distribution. These data open new
avenues for studying underlying connectivity patterns, how they are distributed in the population and if this distribution changes in time or across predictors of interest. Motivated by neuroscience and social science applications, I will discuss some issues associated with available statistical models and I will outline recent methods I proposed to cover some of the current gaps via Bayesian nonparametric models leveraging latent space representations.
 

Practical information

  • Informed public
  • Free

Organizer

  • Prof. Philippe Michel

Contact

  • marcia gouffon

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