We are extremely pleased to invite you to our first in this semester Tuesday seminar of Seminar series in Statistics and Data Science

Speaker:  Camilla Lingjærde, Postdoctoral Fellow at The Norwegian Centre for Knowledge-driven Machine Learning Integreat,  University of Oslo

Title:  Covariate-dependent network modelling with the horseshoe prior

When? Tuesday 08.10.2024, 14:15-15:15

Where?  Erling Svedrups plass and Zoom https://uio.zoom.us/j/62402209920?pwd=YisOD0lynaNtSG1yotZmuVqwEa5vv5.1  

Abstract: Network models are useful tools for modelling complex associations in a vast number of areas, including statistical omics. If a Gaussian graphical model with independent and identically distributed samples is assumed, an association network can be estimated by determining the non-zero entries of the inverse covariance (precision) matrix of the data. This independence assumption is however violated in many applications. For example, observations from multivariate time series data will not be independent due to temporal correlations. Similarly, spatial data will exhibit spatial correlations. Additionally, the identical distribution assumption will often not hold due to heterogeneity in data; a common problem in many applications such as cancer omics, where large tumour heterogeneity in patients with the same cancer type is common due to various genetic and genomic factors. 

In this talk, I will present recent advances and challenges in our work with modelling covariate-dependent Gaussian graphical models through kernel smoothing and the graphical horseshoe prior. This work is a collaboration with Prof. Sylvia Richardson and Dr. Hélène Ruffieux.  

Welcome! 

Best regards,
Thordis Linda Thorarinsdottir & Aliaksandr Hubin

Camilla Lingjærde