Below are three talks running from 1pm: Friday 14 Aug 1pm, Dr Boris Beranger (UNSW Sydney) Composite likelihood and logistic regression models for aggregated data. Friday 14 Aug 2pm, Dr Khue-Dung Dang (UTS) Subsampling Sequential Monte Carlo for Static Bayesian Models. Friday 14 Aug 4pm, Dr Pavel Krupskiy (University of Melbourne) Conditional Normal Extreme-Value Copulas. Composite likelihood and logistic regression models for aggregated data Date: 14 August 2020, Friday Time: 1pm Speaker: Dr Boris Beranger (UNSW Sydney) Abstract: Symbolic data analysis (SDA) is an emerging technique for the analysis of large and complex datasets where individual level data are summarised into group-based distributional summaries (symbols) such as random rectangles or histograms.Likelihood-based methods have been recently developed, allowing to fit models for the underlying data while only observing distributional summaries. However, while powerful, when working with random histograms this approach rapidly becomes computationally intractable as the dimension of the underlying data increases. In this talk we first introduce a composite likelihood setting for the analysis of random histograms in K dimensions using lower-dimensional marginal histograms. We apply this approach to bypass the well known computational issues in the analysis of spatial extremes over large number of spatial locations, and show large computational savings compared to existing model fitting procedures. Logistic regression models are a popular method to predict the probability of categorical response data. However inference for these models can become computationally prohibitive for large datasets. The second part of the talk focuses on summarising a collection of predictor variables into histograms in order to perform inference. Based on composite likelihoods, we derive an efficient one-versus rest approximate composite likelihood model for histogram-value random variables. We demonstrate that this procedure can achieve comparable classification rates than state-of-the-art subsampling algorithms for logistic regression. Link: https://zoom.uts.edu.au/j/91473507261 (Password: BayesStats) Subsampling Sequential Monte Carlo for Static Bayesian Models Date: 14th August 2020 Friday Time: 2-3pm Speaker: Dr. Khue-Dung Dang (UTS) joint work David Gunawan, Matias Quiroz, Robert Kohn, and Minh Ngoc Tran Abstract: We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution. Each update of the particle cloud consists of three steps: reweighting, resampling, and moving. In the move step, each particle is moved using a Markov kernel; this is typically the most computationally expensive part, particularly when the dataset is large. It is crucial to have an efficient move step to ensure particle diversity. Our article makes two important contributions. First, in order to speed up the SMC computation, we use an approximately unbiased and efficient annealed likelihood estimator based on data subsampling. The subsampling approach is more memory efficient than the corresponding full data SMC, which is an advantage for parallel computation. Second, we use a Metropolis within Gibbs kernel with two conditional updates. A Hamiltonian Monte Carlo update makes distant moves for the model parameters, and a block pseudo-marginal proposal is used for the particles corresponding to the auxiliary variables for the data subsampling. We demonstrate both the usefulness and limitations of the methodology for estimating four generalized linear models and a generalized additive model with large datasets. Zoom link: https://uow-au.zoom.us/j/96143600422?pwd=NGs3YUVpd244QndOcUt2Z1RyRmZOdz09 Conditional Normal Extreme-Value Copulas Date: 14 August 2020, Friday Time: 4pm Speaker: Dr Pavel Krupskiy (University of Melbourne) Abstract: We propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independence models, where the dependence among observed variables is modeled using one unobserved factor. Conditional on this factor, the distribution of these variables is given by the Gaussian copula. This structure allows one to build flexible and parsimonious models for data with complex dependence structures, such as data with spatial or temporal dependence. We study the extreme-value limits of these models and show some interesting special cases of the proposed class of copulas. We develop estimation methods for the proposed models and conduct a simulation study to assess the performance of these algorithms. Finally, we applythese copula models to analyze data on monthly wind maxima and stock return minima. Link: https://au.bbcollab.com/guest/fcf219c74ac743e89565a9e6e8d349a9