SMRI Seminar: ’State-space models as graphs’ Victor Elvira (University of Edinburgh) Date and time: Thursday 29 February, 13:00-14:00 AEDT Location: Law Annex Lecture Theatre 026 and online Register/join for online attendance: https://uni-sydney.zoom.us/j/82234629371 Abstract: Modeling and inference in multivariate time series is central in statistics, signal processing, and machine learning. A fundamental question when analyzing multivariate sequences is the search for relationships between their entries (or the modeled hidden states), especially when the inherent structure is a directed (causal) graph. In such context, graphical modeling combined with sparsity constraints allows to limit the proliferation of parameters and enables a compact data representation which is easier to interpret in applications, e.g., in inferring causal relationships of physical processes in a Granger sense. In this talk, we present a novel perspective consisting on state-space models being interpreted as graphs. Then, we propose novel algorithms that exploit this new perspective for the estimation of the linear matrix operator and also the covariance matrix in the state equation of a linear-Gaussian state-space model. Finally, we discuss the extension of this perspective for the estimation of other model parameters in more complicated models. ---- Please join us after the seminar for SMRI afternoon tea, 2:00-2:45pm every Thursday on the SMRI Terrace (accessed through A14-04-L4.36) ---- Current and past seminar info (including recordings) can be found on the seminars webpage. Other upcoming SMRI events can be found here: https://mathematical-research-institute.sydney.edu.au/news-events/ SMRI YouTube Channel: https://youtube.com/@SydMathInst