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Seminar 23 March @ 10 am AEDT

  Clustering of large deviations in moving average processes: short and long memory regimes Date: 23 March 2023, Thursday Time: 10am AEDT Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University jointly organized with the Probability Victoria Seminar. Contact the organizers: Kostya Borovkov, Andriy Olenko Speaker: Prof  Gennady Samorodnitsky  (Cornell University, United States of America) Abstract: We describe the cluster of large deviations events that arise when one such large deviations event occurs. We work in the framework of an infinite moving average process with a noise that has finite exponential moments. The cluster turns out to have different shapes in the cases when the moving average process has short memory and long memory. Joint work with Arijit Chakrabarty. Zoom meeting link:
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Seminar @2pm 24 Feb

Valid inference after clustering with application to single-cell RNA-sequencing data Speaker: Lucy Gao, University of British Columbia Zoom link: Abstract: In single-cell RNA-sequencing studies, researchers often model the variation between cells with a latent variable, such as cell type or pseudotime, and investigate associations between the genes and the latent variable. As the latent variable is unobserved, a two-step procedure seems natural: first estimate the latent variable, then test the genes for association with the estimated latent variable. However, if the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to control the type I error rate. In my talk, I will introduce two different approaches to this problem. First, I will apply ideas from selective inference to develop a valid test for a difference in means between clusters obtained from the hierarchical clustering alg

UNSW Stats seminar March schedule

March Schedule Friday 3 March, 4 PM AET Roman Gauriot, Senior Lecturer in Economics, Deakin University Virtual Zoom link: “How Market Prices React to Information: Evidence from Binary Options Markets”

Seminar @ 2pm 2nd February (Hybrid Mode)

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 algorithm costs O(N)

Seminar 10 January @ 8:00 (UTC)

  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 ( 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 discuss the p

Seminar 13 December @ 16:00 (UTC)

  Getting something out of nothing:  topological data analysis for cosmology. Date: Tuesday, 13 December 2022 Time: 16:00 (UTC) Speaker:  Dr Jessi Cisewski Kehe (University of Wisconsin, USA) Contact the organizer: Andriy Olenko Abstract: The transference from data to information is a key component of many areas of research in astronomy and cosmology. This process can be challenging when data exhibit complicated spatial structures, such as the large-scale structure (LSS) of the Universe. Methods that target shape-related features may be helpful for summarizing qualitative properties that are not retrieved with standard techniques. Topological data analysis (TDA) provides a framework for quantifying shape-related properties of data. Persistent homology is a popular TDA tool that offers a procedure to represent, visualize, and interpret complex data by extracting topological features which may be used to infer properties of the underlying structures. Persistent