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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
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of interest include: infectious disease dynamics in wild and domestic animal populations wildlife diseases and conservation network analysis of disease spread phylodynamics model-based statistical inference
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detection model to a more flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques
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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
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Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum
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the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms
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. Among the approaches used will be the Bayesian information-theoretic Minimum Message Length (MML) principle (Wallace and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) References: Wallace, C.S