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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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a Bayesian perspective, the use of skew error distributions from well-known skewing mechanisms (eg Azzalini, 1985; Ley 2010), while allowing the parameter(s) controlling the skewness to vary over time
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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silico model of normal development. Bayesian inference will calibrate model parameters and highlight control points, with predictive accuracy benchmarked against existing perturbation datasets. O3. Map
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environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple
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Interview Motivated in learning new methodologies and applying new knowledge Essential Interview Knowledge of the approximate Bayesian machine learning (e.g. MCMC) (assessed at: Application form/Interview
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campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
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Bastian, C. C. (2018). Working memory updating and binding training: Bayesian evidence supporting the absence of transfer. Journal of Experimental Psychology: General, 147(6), 829-858. https://doi.org