Results 21 to 30 of about 436,673 (317)
Bayesian Inference in Numerical Cognition: A Tutorial Using JASP
Journal of Numerical Cognition, 2020 Researchers in numerical cognition rely on hypothesis testing and parameter estimation to evaluate the evidential value of data. Though there has been increased interest in Bayesian statistics as an alternative to the classical, frequentist approach to ...
Thomas J. Faulkenberry +2 more
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Stats, 2022 In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey’s prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the ML and ...
Tzong-Ru Tsai +3 more
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Enhanced off-grid DOA estimation by corrected power Bayesian inference using difference coarray
The Journal of Engineering, 2019 Sparse Bayesian inference for on-grid direction-of-arrival (DOA) estimation using difference coarray was investigated in the authors’ previous work to estimate more signal sources than the number of physical antenna elements. Sparse Bayesian inference is
Yanan Ma, Xianbin Cao, Xiangrong Wang
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Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]
PLoS ONE, 2014 Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
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Journal of Statistical Software, 2022 Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models,
Alexandre Bouchard-Côté +7 more
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Variational Inference for Nonlinear Structural Identification [PDF]
Journal of Applied and Computational Mechanics, 2021 Research interest in predictive modeling within the structural engineering community has recently been focused on Bayesian inference methods, with particular emphasis on analytical and sampling approaches. In this study, we explore variational inference,
Alana Lund +2 more
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BMC Evolutionary Biology, 2005 Background Bayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets.
Harlow Timothy J +2 more
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Universal Darwinism as a process of Bayesian inference
Frontiers in Systems Neuroscience, 2016 Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these ...
John Oberon Campbell
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A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network
Micromachines, 2023 Memristor crossbar arrays are a promising platform for neuromorphic computing. In practical scenarios, the synapse weights represented by the memristors for the underlying system are subject to process variations, in which the programmed weight when read
Di Gao, Xiaoru Xie, Dongxu Wei
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Fuzzy Bayesian Inference [PDF]
, 2009 Data are frequently not precise numbers but more or less non-precise, also called fuzzy. Moreover a-priori information in Bayesian inference is usually not available as a precise probability distribution. In case of fuzzy data and fuzzy a-priori information Bayes' theorem has to be generalized.
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