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Relevant bibliographies by topics / Bayesian paradigms

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Journal articles

Academic literature on the topic 'Bayesian paradigms'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Bayesian paradigms"

1

Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (1997): 3–33. http://dx.doi.org/10.1142/s0218001497000020.

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A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful

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2

Oaksford, Mike, and Nick Chater. "New Paradigms in the Psychology of Reasoning." Annual Review of Psychology 71, no. 1 (2020): 305–30. http://dx.doi.org/10.1146/annurev-psych-010419-051132.

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The psychology of verbal reasoning initially compared performance with classical logic. In the last 25 years, a new paradigm has arisen, which focuses on knowledge-rich reasoning for communication and persuasion and is typically modeled using Bayesian probability theory rather than logic. This paradigm provides a new perspective on argumentation, explaining the rational persuasiveness of arguments that are logical fallacies. It also helps explain how and why people stray from logic when given deductive reasoning tasks. What appear to be erroneous responses, when compared against logic, often t

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Neupert, Shevaun D., Claire M. Growney, Xianghe Zhu, Julia K. Sorensen, Emily L. Smith, and Jan Hannig. "BFF: Bayesian, Fiducial, and Frequentist Analysis of Cognitive Engagement among Cognitively Impaired Older Adults." Entropy 23, no. 4 (2021): 428. http://dx.doi.org/10.3390/e23040428.

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Engagement in cognitively demanding activities is beneficial to preserving cognitive health. Our goal was to demonstrate the utility of frequentist, Bayesian, and fiducial statistical methods for evaluating the robustness of effects in identifying factors that contribute to cognitive engagement for older adults experiencing cognitive decline. We collected a total of 504 observations across two longitudinal waves of data from 28 cognitively impaired older adults. Participants’ systolic blood pressure responsivity, an index of cognitive engagement, was continuously sampled during cognitive testi

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4

Ly, Alexander, Akash Raj, Alexander Etz, Maarten Marsman, Quentin F. Gronau, and Eric-Jan Wagenmakers. "Bayesian Reanalyses From Summary Statistics: A Guide for Academic Consumers." Advances in Methods and Practices in Psychological Science 1, no. 3 (2018): 367–74. http://dx.doi.org/10.1177/2515245918779348.

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Across the social sciences, researchers have overwhelmingly used the classical statistical paradigm to draw conclusions from data, often focusing heavily on a single number: p. Recent years, however, have witnessed a surge of interest in an alternative statistical paradigm: Bayesian inference, in which probabilities are attached to parameters and models. We feel it is informative to provide statistical conclusions that go beyond a single number, and—regardless of one’s statistical preference—it can be prudent to report the results from both the classical and the Bayesian paradigms. In order to

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5

Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing missing always at random in multivariate data." Biometrika 107, no. 1 (2019): 246–53. http://dx.doi.org/10.1093/biomet/asz061.

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Summary Models for analysing multivariate datasets with missing values require strong, often unassessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable, which is a two-fold assumption dependent on the mode of inference. The first part, which is the focus here, under the Bayesian and direct-likelihood paradigms requires that the missing data be missing at random; in contrast, the frequentist-likelihood paradigm demands that the missing data mechanism always produce missing at random data, a condition known as missing always at random. Un

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6

Alotaibi, Refah, Lamya A. Baharith, Ehab M. Almetwally, Mervat Khalifa, Indranil Ghosh, and Hoda Rezk. "Statistical Inference on a Finite Mixture of Exponentiated Kumaraswamy-G Distributions with Progressive Type II Censoring Using Bladder Cancer Data." Mathematics 10, no. 15 (2022): 2800. http://dx.doi.org/10.3390/math10152800.

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A new family of distributions called the mixture of the exponentiated Kumaraswamy-G (henceforth, in short, ExpKum-G) class is developed. We consider Weibull distribution as the baseline (G) distribution to propose and study this special sub-model, which we call the exponentiated Kumaraswamy Weibull distribution. Several useful statistical properties of the proposed ExpKum-G distribution are derived. Under the classical paradigm, we consider the maximum likelihood estimation under progressive type II censoring to estimate the model parameters. Under the Bayesian paradigm, independent gamma prio

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Neupert, Shevaun D., and Jan Hannig. "BFF: Bayesian, Fiducial, Frequentist Analysis of Age Effects in Daily Diary Data." Journals of Gerontology: Series B 75, no. 1 (2019): 67–79. http://dx.doi.org/10.1093/geronb/gbz100.

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Abstract Objectives We apply new statistical models to daily diary data to advance both methodological and conceptual goals. We examine age effects in within-person slopes in daily diary data and introduce Generalized Fiducial Inference (GFI), which provides a compromise between frequentist and Bayesian inference. We use daily stressor exposure data across six domains to generate within-person emotional reactivity slopes with daily negative affect. We test for systematic age differences and similarities in these reactivity slopes, which are inconsistent in previous research. Method One hundred

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8

Kabanda, Gabriel. "Bayesian Network Model for a Zimbabwean Cybersecurity System." Oriental journal of computer science and technology 12, no. 4 (2020): 147–67. http://dx.doi.org/10.13005/ojcst12.04.02.

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The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an a

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9

Laurens, Jean, Dominik Straumann, and Bernhard J. M. Hess. "Processing of Angular Motion and Gravity Information Through an Internal Model." Journal of Neurophysiology 104, no. 3 (2010): 1370–81. http://dx.doi.org/10.1152/jn.00143.2010.

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The vestibular organs in the base of the skull provide important information about head orientation and motion in space. Previous studies have suggested that both angular velocity information from the semicircular canals and information about head orientation and translation from the otolith organs are centrally processed in an internal model of head motion, using the principles of optimal estimation. This concept has been successfully applied to model behavioral responses to classical vestibular motion paradigms. This study measured the dynamic of the vestibuloocular reflex during postrotator

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Guo, Jeff, Bojana Ranković, and Philippe Schwaller. "Bayesian Optimization for Chemical Reactions." CHIMIA 77, no. 1/2 (2023): 31. http://dx.doi.org/10.2533/chimia.2023.31.

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Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence, representing an enormous resource sink. Bayesian optimization (BO) is an optimization algorithm that recommends the next experiment based on previous observations and has recently gained considerable interest in the general chemistry community. The application of BO for chemical reactions has been demonstrated to increase efficiency in optim

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