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viewpoint biases/opinions and support epistemic uncertainty. Can we disentangle the biases/opinions of a diversity of sources? We could adapt Bayesian meta-reasoning and work with the LLM in an agentic
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perspective to fundamentally solve the central question: how should an observer act in an environment to actively uncover the goal of the agent? Required knowledge Proficiency in Programming, Bayesian
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discovery. Bayesian approaches provide a principled framework for modeling uncertainty by capturing posterior distributions over model parameters or predictions. Despite recent progress in approximate
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networks, Bayesian inference, computational neuroscience, mathematics.
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of theoretical work asks whether such a system could serve as a runtime guardrail, deriving probabilistic bounds on the harm probability of a candidate agent action by reasoning over a Bayesian
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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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that explicitly account for drift and open-set contamination. O2: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal
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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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, Joshua W. and D.L. Dowe (2005). ``Minimum Message Length and Generalized Bayesian Nets with Asymmetric Languages'', Chapter 11 (pp265-294) in P. Gru:nwald, I. J. Myung and M. A. Pitt (eds.), Advances in
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ecosystem interactions. If used wisely for decision-support, these technologies can help select and implement effective policies. This PhD project, jointly offered by Monash University (Australia) and