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March 2020

BSU Seminar Series: “Squeezing the most out of ridge”

March 3 @ 2:00 pm - 3:00 pm
Large Seminar Room, IPH, Institute of Public Health, Forvie Site
Cambridge, CB2 0SR United Kingdom
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Title: "Squeezing the most out of ridge" Speaker: Professor Mark van de Wiel, Amsterdam University Medical Center Abstract: Ridge regression is nowadays regarded as a somewhat old-fashioned technique: most statisticians prefer sparse models, while machine learners use non-linear, multi-layer prediction algorithms. The aim of this talk is to endorse ridge by illustrating its strengths, from a methodological, computational and applied perspective, including: Flexibility (in terms of output and type of covariates) Bayesian counterpart facilitates empirical Bayes estimation of penalty parameter(s)…

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April 2020

Virtual Seminar: “Including expert knowledge in genomic selection through intuitive tree-based joint priors”

April 16 @ 2:00 pm - 3:00 pm
Virtual Seminar via MS Teams

Speaker: Ingeborg Hem, Norwegian University of Science and Technology Title: "Including expert knowledge in genomic selection through intuitive tree-based joint priors" Abstract: Bayesian hierarchical models with additive latent structures are popular since additivity simplifies interpretation and inference. However, the common choice of independent priors on the variances of the model components result in haphazard a priori control on the total variance and how it is attributed to the model components. We propose a joint prior for the variance parameters that…

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May 2020

Virtual Seminar: “Delayed-acceptance Sequential Monte Carlo”

May 21 @ 10:00 am - 11:00 am
Virtual Seminar via MS Teams

Speaker: Joshua Bon, Queensland University of Technology Title: Delayed-acceptance Sequential Monte Carlo Abstract:¬†Delayed-acceptance is a technique for reducing computational effort for Bayesian models with expensive likelihoods. Using delayed-acceptance kernels in MCMC can reduce the number of expensive likelihoods evaluations required to approximate a posterior expectation to a given accuracy. It uses a surrogate, or approximate, likelihood to avoid evaluation of the expensive likelihood when possible. Importantly, delayed-acceptance kernels preserve the intended targeted distribution of the Markov chain, when viewed as…

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June 2020

Virtual BSU Seminar: ‘Parameterizing Causal Marginal Models’

June 16 @ 2:00 pm - 3:00 pm
Virtual Seminar via MS Teams

Speaker: Prof Robin Evans, University of Oxford Title: 'Parameterizing Causal Marginal Models' Abstract: Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the quantity of interest is often a marginal quantity of this other probability distribution.¬† This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. Naive attempts to specify a model parametrically can lead to unwanted consequences such as…

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