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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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is known as 'Blackbox Multi-Objective Optimization for Unknown Functions', which will help the users (e.g., scientists) to explore the input space of their experiments (i.e., x) that maximizes
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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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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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and statistics, with expertise spanning time series analysis, Bayesian inference, financial econometrics, and data analytics. As home to one of the strongest forecasting research groups worldwide, we
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funding. Candidate Requirements The successful candidate will require a proven track record in research fieldwork and historical methodology. In its assessment, the selection committee will prioritise
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an attractive remuneration and relocation package. 1. Professor, Monash Health and Climate Initiative We are seeking a distinguished academic with an outstanding track record in climate change and health
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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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Fellow is responsible for contributing to the advancement of the University's research objectives through focused work on an Australian Research Council funded Research Hub for Carbon Utilisation and