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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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, including working with multivariate data. Substantial experience in code development and, preferably, experience with Bayesian statistical modelling techniques and software. Willing to develop expert
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statistical, Bayesian, and deep-learning approaches. Lead improvements in data quality, integration, and reproducibility across multi-centre trials and registries. Collaborate with leading clinicians, engineers
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. Butler, C. Goncu, and L. Holloway. Tactile presentation of network data: Text, matrix or diagram? In CHI2020, pages 1–12, 2020. I. Zukerman et al.˙Exploratory Interaction with a Bayesian Argumentation
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will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how environmental factors and antifouling technologies