Statistics Across Campuses

Backfitting for large scale crossed random effects regressions Date: 2 February 2022, Thursday Time: 2pm AEDT Speaker: Prof Art Owens (Stanford Uni)  Abstract: Large scale genomic and electronic commerce data sets often have a crossed random effects structure, arising from genotypes x environments or customers x products.  Naive methods of handling such data will produce inferences that do not generalize. Regression models that properly account for crossed random effects can be very expensive to compute. The cost of both generalized least squares and Gibbs sampling can easily grow as N^(3/2) (or worse) for N observations. Papaspiliopoulos, Roberts and Zanella (2020) present a collapsed Gibbs sampler that costs O(N), but under an extremely stringent sampling model. We propose a backfitting algorithm to compute a generalized least squares estimate and prove that it costs O(N) under greatly relaxed though still strict sampling assumptions. Empirically, the backfitting algori...

  Galaxy Merger Reconstruction with Generative Graph Neural Networks Date: Tuesday, 10 January 2023 Time: 8:00 UTC ( 7:00 pm AEST ) Speaker:  A/Prof Yuan-Sen Ting (Australian National University, Australia) Contact the organizer: Andriy Olenko (a.olenko@latrobe.edu.au) Abstract:   A key yet unresolved question in modern-day astronomy is how galaxies formed and evolved. The quest to understand how galaxies evolve has led many semi-analytic models to infer the galaxy properties from their merger history. However, most classical approaches rely on studying the global connection between dark matter haloes and galaxies, often reducing the study to crude summary statistics. The recent advancement in graph neural networks might open up many new possibilities; graphs are a natural descriptor of galaxy progenitor systems – any progenitor system at a high redshift can be regarded as a graph, with individual progenitors as nodes on the graph. In this presentation, I will ...