Results 11 to 20 of about 1,249,090 (302)
Eryn : A multi-purpose sampler for Bayesian inference [PDF]
, 2023 In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case.
N. Karnesis +4 more
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Robust generalised Bayesian inference for intractable likelihoods [PDF]
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2021 Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis‐specification of the likelihood.
Takuo Matsubara +3 more
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Adaptive User Interfaces and the Use of Inference Methods
Computational Science and Techniques, 2021 Bayesian Networks are used to model a user's behaviour. There is not much research on the use of Frequentist Inference to accomplish this same task.
Rachelle Barrette, Ratvinder Grewal
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Bayesian causal inference: a critical review [PDF]
Philosophical Transactions of the Royal Society A, 2022 This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects and ...
Fan-qun Li, Peng Ding, F. Mealli
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RL with KL penalties is better viewed as Bayesian inference [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 Reinforcement learning (RL) is frequently employed in fine-tuning large language models (LMs), such as GPT-3, to penalize them for undesirable features of generated sequences, such as offensiveness, social bias, harmfulness or falsehood.
Tomasz Korbak, Ethan Perez, C. Buckley
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Randomization-based, Bayesian inference of causal effects
Journal of Causal Inference, 2023 Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet causal inferences from randomized experiments are especially credible because they depend on a known assignment process, not a ...
Leavitt Thomas
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Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue [PDF]
Monthly notices of the Royal Astronomical Society, 2020 Gravitational waves provide a unique tool for observational astronomy. While the first LIGO–Virgo catalogue of gravitational-wave transients (GWTC-1) contains 11 signals from black hole and neutron star binaries, the number of observations is ...
I. Romero-Shaw +62 more
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Machine learning and Bayesian inference in nuclear fusion research: an overview
Plasma Physics and Controlled Fusion, 2023 This article reviews applications of Bayesian inference and machine learning (ML) in nuclear fusion research. Current and next-generation nuclear fusion experiments require analysis and modelling efforts that integrate different models consistently and ...
A. Pavone +3 more
semanticscholar +1 more source
, 2010 This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal method for summarising uncertainty and making estimates and predictions using probability statements conditional on observed data and an assumed model (Gelman 2008). The Bayesian perspective is thus applicable to all aspects of statistical inference, while
Jean-Michel Marin +2 more
openaire +3 more sources
New Frontiers in Bayesian Modeling Using the INLA Package in R
Journal of Statistical Software, 2021 The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models.
Janet Van Niekerk +3 more
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