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Neural Computation, 2021 Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design—can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on
Friston, K. +4 more
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Proceedings of the National Academy of Sciences, 2020 Significance Most statistical methods rely on certain mathematical conditions, known as regularity assumptions, to ensure their validity. Without these conditions, statistical quantities like P values and confidence intervals might not be valid.
Larry Wasserman +2 more
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The Quarterly Journal of Economics, 2018 Abstract Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner’s curse, since we are more likely to select a given option precisely when we overestimate its effectiveness.
Toru Kitagawa +2 more
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, 2023 Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. We integrate normative and
Raju, Rajkumar Vasudeva +3 more
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Visualization and Inferences [PDF]
Harvard Data Science Review, 2021 International ...
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2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2013 Lower bounds for the average probability of error of estimating a hidden variable X given an observation of a correlated random variable Y, and Fano's inequality in particular, play a central role in information theory. In this paper, we present a lower bound for the average estimation error based on the marginal distribution of X and the principal ...
Medard, Muriel +5 more
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Foundations of Inference [PDF]
Axioms, 2012 We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries.
Kevin H. Knuth, John Skilling
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Variational Inference for Logical Inference
arXiv: Computation and Language, 2017 Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth. Here we make two contributions to this framework. The first is to show how a type of logical inference can be performed by evaluating conditional probabilities.
Emerson, Guy, Copestake, Ann
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Distributional inference [PDF]
, 1995 The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction.
Kroese, A.H. +3 more
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How do medical researchers make causal inferences? [PDF]
, 2019 Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it.
Dammann, Olaf +2 more
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