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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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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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confidence estimation pipelines for LLM-generated causal claims [4, 5], so that the world model's outputs come with conformal-style probabilistic bounds that can be plugged directly into Bayesian-oracle-style
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that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying
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of: Developing model visualisation methods to enable interactive and dynamic model building. Robust selection and estimation in regression type models. Variable selection and inference for statistically
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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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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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, 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