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Journal articles on the topic 'Bayesian paradigms'

Author: Grafiati

Published: 6 September 2023

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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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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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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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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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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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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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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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Ateeq, Kahkashan, Noumana Safdar, and Shakeel Ahmed. "Exploring the Exponentiated Transmuted Inverse Rayleigh Distribution (ETIRD) in Classical and Bayesian Paradigms." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 4, no. 2 (2022): 17–37. http://dx.doi.org/10.52700/scir.v4i2.114.

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We derived, a new three parameters continuous probability distribution called Exponentiated Transmuted Inverse Rayleigh Distribution (ETIRD). Various mathematical properties of the new distribution including mean, rth moments, moment generating function, quantile function etc. are derived. In the Classical paradigm, the estimators of the distribution are obtained using the maximum likelihood method. The Bayes estimators are derived under square error loss function (SELF) using non-informative and informative priors via the Lindley approximation technique. Bayes Estimators are compared with the

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Jacobsen, Daniel J., Lars Kai Hansen, and Kristoffer Hougaard Madsen. "Bayesian Model Comparison in Nonlinear BOLD fMRI Hemodynamics." Neural Computation 20, no. 3 (2008): 738–55. http://dx.doi.org/10.1162/neco.2007.07-06-282.

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Nonlinear hemodynamic models express the BOLD (blood oxygenation level dependent) signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for both the neural activity and the hemodynamics. We compare two such combined models: the original balloon model with a square-pulse neural model (Friston, Mechelli, Turner, & Price, 2000) and an extended balloon model with a more sophisticated neural model (Buxton, Uludag, Dubowitz, & Liu, 2004). We learn the parameters of both models using a Bayesian approach, where the dis

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Levy, Roy. "The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling." Journal of Probability and Statistics 2009 (2009): 1–18. http://dx.doi.org/10.1155/2009/537139.

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Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the estimation of models that might otherwise be intractable, and frees the researcher from certain possible misconceptions about the models.

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Hashemi, Meysam, Anirudh N. Vattikonda, Viktor Sip, et al. "On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread." PLOS Computational Biology 17, no. 7 (2021): e1009129. http://dx.doi.org/10.1371/journal.pcbi.1009129.

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Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, whi

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Zhong, Hongye, and Jitian Xiao. "Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm." Scientific Programming 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/1901876.

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With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves th

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Ouyang, Guang, Joseph Dien, and Romy Lorenz. "Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration." Journal of Neural Engineering 19, no. 1 (2022): 016016. http://dx.doi.org/10.1088/1741-2552/ac42b6.

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Abstract Objective. Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because the

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Chinembiri, Tsikai Solomon, Onisimo Mutanga, and Timothy Dube. "Carbon Stock Prediction in Managed Forest Ecosystems Using Bayesian and Frequentist Geostatistical Techniques and New Generation Remote Sensing Metrics." Remote Sensing 15, no. 6 (2023): 1649. http://dx.doi.org/10.3390/rs15061649.

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The study compares the performance of a hierarchical Bayesian geostatistical methodology with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for predicting C stock using prediction aides from the Landsat-8 and Sentinel-2 multispectral remote sensing platforms. The frequentist geostatistical approach’s reliance on the long-run frequency of repeated experiments for constructing confidence intervals is not always practical or feasible, as practitioners typically have access to a single dataset due to cost constraints on surveys and sampling. We evaluated t

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Ateeq, Kahkashan, Saima Altaf, and Muhammad Aslam. "Modeling and Bayesian Analysis of Time between the Breakdown of Electric Feeders." Modelling and Simulation in Engineering 2022 (May 30, 2022): 1–13. http://dx.doi.org/10.1155/2022/5830945.

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The failure of electric feeders is a common problem in the summer season in Pakistan. In this article, one of the troubling aspects of the electric power system of Pakistan (Multan city) has been studied. The time lapses between the breakdown of electric feeders of the city have been modeled by suggesting an inverse Rayleigh-exponential distribution. The parameters of the distribution are estimated in both the frequentist and Bayesian paradigms. Since the Bayes estimators under informative priors are not attained in the closed form, this paper provides a comparative analysis of the Bayes estim

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Puttick, Mark N., Joseph E. O'Reilly, Alastair R. Tanner, et al. "Uncertain-tree: discriminating among competing approaches to the phylogenetic analysis of phenotype data." Proceedings of the Royal Society B: Biological Sciences 284, no. 1846 (2017): 20162290. http://dx.doi.org/10.1098/rspb.2016.2290.

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Morphological data provide the only means of classifying the majority of life's history, but the choice between competing phylogenetic methods for the analysis of morphology is unclear. Traditionally, parsimony methods have been favoured but recent studies have shown that these approaches are less accurate than the Bayesian implementation of the Mk model. Here we expand on these findings in several ways: we assess the impact of tree shape and maximum-likelihood estimation using the Mk model, as well as analysing data composed of both binary and multistate characters. We find that all methods s

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Zhang, Zhihao, Saksham Chandra, Andrew Kayser, Ming Hsu, and Joshua L. Warren. "A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model." Computational Psychiatry 4 (August 2020): 40–60. http://dx.doi.org/10.1162/cpsy_a_00028.

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Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations.

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van de Wouw, Didrika S., Ryan T. McKay, Bruno B. Averbeck, and Nicholas Furl. "Explaining human sampling rates across different decision domains." Judgment and Decision Making 17, no. 3 (2022): 487–512. http://dx.doi.org/10.1017/s1930297500003557.

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AbstractUndersampling biases are common in the optimal stopping literature, especially for economic full choice problems. Among these kinds of number-based studies, the moments of the distribution of values that generates the options (i.e., the generating distribution) seem to influence participants’ sampling rate. However, a recent study reported an oversampling bias on a different kind of optimal stopping task: where participants chose potential romantic partners from images of faces (Furl et al., 2019). The authors hypothesised that this oversampling bias might be specific to mate choice. W

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Yang, Geunbo, Wongyu Lee, Youjung Seo, et al. "Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons." Sensors 23, no. 16 (2023): 7232. http://dx.doi.org/10.3390/s23167232.

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A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed networ

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Zhang, Xuan, Ole-Christoffer Granmo, and B. John Oommen. "On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata." Applied Intelligence 39, no. 4 (2013): 782–92. http://dx.doi.org/10.1007/s10489-013-0424-x.

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Griffiths, Thomas L., Michael L. Kalish, and Stephan Lewandowsky. "Theoretical and empirical evidence for the impact of inductive biases on cultural evolution." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1509 (2008): 3503–14. http://dx.doi.org/10.1098/rstb.2008.0146.

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The question of how much the outcomes of cultural evolution are shaped by the cognitive capacities of human learners has been explored in several disciplines, including psychology, anthropology and linguistics. We address this question through a detailed investigation of transmission chains, in which each person passes information to another along a chain. We review mathematical and empirical evidence that shows that under general conditions, and across experimental paradigms, the information passed along transmission chains will be affected by the inductive biases of the people involved—the c

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FEAR, CHRISTOPHER F., and DAVID HEALY. "Probabilistic reasoning in obsessive–compulsive and delusional disorders." Psychological Medicine 27, no. 1 (1997): 199–208. http://dx.doi.org/10.1017/s0033291796004175.

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Background. Delusional disorder (DD) and obsessive–compulsive disorder (OCD) have been investigated in previous studies using probabilistic reasoning paradigms and abnormalities in each group have been reported. No study to date has compared results between these groups. This study compares patients with these disorders with those who have both phenomena.Methods. Thirty subjects with DD, 29 with OCD and 16 with obsessive and delusional features were compared with 30 normal controls in a study of probabilistic reasoning using two different computer-based tasks involving a Bayesian paradigm.Resu

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Bouslama, Mehdi, Meredith T. Bowen, Diogo C. Haussen, et al. "Selection Paradigms for Large Vessel Occlusion Acute Ischemic Stroke Endovascular Therapy." Cerebrovascular Diseases 44, no. 5-6 (2017): 277–84. http://dx.doi.org/10.1159/000478537.

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Background: Optimal patient selection methods for thrombectomy in large vessel occlusion stroke (LVOS) are yet to be established. We sought to evaluate the ability of different selection paradigms to predict favorable outcomes. Methods: Review of a prospectively collected database of endovascular patients with anterior circulation LVOS, adequate CT perfusion (CTP), National Institutes of Health Stroke Scale (NIHSS) ≥10 from September 2010 to March 2016. Patients were retrospectively assessed for thrombectomy eligibility by 4 mismatch criteria: Perfusion-Imaging Mismatch (PIM): between CTP-deri

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Li, Man, Feng Li, Jiahui Pan, et al. "The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning." Sensors 21, no. 5 (2021): 1613. http://dx.doi.org/10.3390/s21051613.

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In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system w

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Wang, Liwei, Xiong Li, Zhuowen Tu, and Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.

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Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we expl

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Lopes, Marcos Venícios de Oliveira, Viviane Martins da Silva, and Thelma Leite de Araujo. "A análise de diagnósticos de enfermagem sob uma perspectiva bayesiana." Revista da Escola de Enfermagem da USP 46, no. 4 (2012): 994–1000. http://dx.doi.org/10.1590/s0080-62342012000400030.

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O uso de técnicas de estatística bayesiana é uma abordagem que tem sido bem aceita e estabelecida em campos fora da enfermagem como um paradigma de redução da incerteza presente em uma dada situação clínica. O presente artigo tem como propósito apresentar um direcionamento para o uso específico do paradigma bayesiano na análise de diagnósticos de enfermagem. Para isso, as etapas e interpretações de análise bayesiana são discutidas; um exemplo teórico e outro prático sobre análise bayesiana de diagnósticos de enfermagem são apresentados; e há a descrição de como a abordagem bayesiana pode ser u

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Hernández, Felipe, and Xu Liang. "Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting." Hydrology and Earth System Sciences 22, no. 11 (2018): 5759–79. http://dx.doi.org/10.5194/hess-22-5759-2018.

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Abstract. The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main existing frameworks for the determination of the models' initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models – mainly related to model indeterminacy and steep computational requirements. In this article we introduce a

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Yu, Tianyuan, Yongxin Yang, Da Li, Timothy Hospedales, and Tao Xiang. "Simple and Effective Stochastic Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3252–60. http://dx.doi.org/10.1609/aaai.v35i4.16436.

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Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning. These neural network variants impact several major considerations, including generalization, network compression, robustness against adversarial attack and label noise, and model calibration. However, many existing networks are complicated and expensive to train, and/or only address one or two of these practical considerations. In this paper we propose a simple and effect

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Abdellah, Ali R., Omar Abdulkareem Mahmood, Ruslan Kirichek, Alexander Paramonov, and Andrey Koucheryavy. "Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet." Future Internet 13, no. 12 (2021): 304. http://dx.doi.org/10.3390/fi13120304.

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The next-generation cellular systems, including fifth-generation cellular systems (5G), are empowered with the recent advances in artificial intelligence (AI) and other recent paradigms. The internet of things (IoT) and the tactile internet are paradigms that can be empowered with AI solutions and integrated with 5G systems to deliver novel services that impact the future. Machine learning technologies (ML) can understand examples of nonlinearity from the environment and are suitable for network traffic prediction. Network traffic prediction is one of the most active research areas that integr

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Crawford, Lindsay, Liye Zou, and Paul D. Loprinzi. "Oxygenation of the Prefrontal Cortex during Memory Interference." Journal of Clinical Medicine 8, no. 12 (2019): 2055. http://dx.doi.org/10.3390/jcm8122055.

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Background: Memory interference occurs when information (or memory) to be retrieved is interrupted by competing stimuli. Proactive interference (PI) occurs when previously acquired information interferes with newly acquired information, whereas retroactive interference (RI) occurs when newly acquired information interferes with previously acquired information. In animal paradigms, the prefrontal cortex (PFC) has been shown to help facilitate pattern separation, and ultimately, attenuate memory interference. Research evaluating the role of the PFC on memory interference among humans is, however

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AVEN, TERJE. "RISK ANALYSIS AND SCIENCE." International Journal of Reliability, Quality and Safety Engineering 11, no. 01 (2004): 1–15. http://dx.doi.org/10.1142/s0218539304001300.

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In this paper we discuss the scientific basis of risk analysis, when a Bayesian approach is the foundation of the analysis. We argue that the analysis cannot be judged by reference to the traditional science paradigms alone, such as the natural sciences, social sciences, mathematics and probability theory. There is a need for recognitions of a risk analysis science which is related to the establishment of principles, methods and models to analysis, describe and communicate risk, in a decision-making context. The "goodness" of these principles, methods and models cannot be evaluated by referenc

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Holbrook, Andrew J., Xiang Ji, and Marc A. Suchard. "From viral evolution to spatial contagion: a biologically modulated Hawkes model." Bioinformatics 38, no. 7 (2022): 1846–56. http://dx.doi.org/10.1093/bioinformatics/btac027.

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Abstract Summary Mutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen’s evolution to its spatial contagion dynamics—effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling—and apply this phylogenetic Hawkes process to a Bayesian analysis of 23 421 viral cases from the 2014 to 2016 Ebola outbreak in West Africa. The proposed model i

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Lavenir, Gabrielle, and Nicolas Bourgeois. "Old people, video games and french press: A topic model approach on a study about discipline, entertainment and self-improvement." MedieKultur: Journal of media and communication research 33, no. 63 (2017): 20. http://dx.doi.org/10.7146/mediekultur.v33i63.24749.

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Over the past few years, the French mainstream press has paid more and more attention to "silver gamers", adults over sixty who play video games. This article investigates the discursive and normative paradigms that underlie the unexpected enthusiasm of the French mainstream press for older adults who play video games. We use mixed methods on a corpus of French, Swiss and Belgian articles that mention both older people and video games. First, we produce topics, that is, sets of words related by their meanings and identified with a Bayesian statistical algorithm. Second, we cross the topic mode

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Kroneisen, Meike, and Daniel W. Heck. "Interindividual Differences in the Sensitivity for Consequences, Moral Norms, and Preferences for Inaction: Relating Basic Personality Traits to the CNI Model." Personality and Social Psychology Bulletin 46, no. 7 (2019): 1013–26. http://dx.doi.org/10.1177/0146167219893994.

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Research on moral decision making usually focuses on two ethical principles: the principle of utilitarianism (= morality of an action is determined by its consequences) and the principle of deontology (= morality of an action is valued according to the adherence to moral norms regardless of the consequences). Criticism on traditional moral dilemma research includes the reproach that consequences and norms are confounded in standard paradigms. As a remedy, a multinomial model (the CNI model) was developed to disentangle and measure sensitivity to consequences ( C), sensitivity to moral norms (

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NAGARAJAN, RADHAKRISHNAN, JANE E. AUBIN, and CHARLOTTE A. PETERSON. "ROBUST DEPENDENCIES AND STRUCTURES IN STEM CELL DIFFERENTIATION." International Journal of Bifurcation and Chaos 15, no. 04 (2005): 1503–14. http://dx.doi.org/10.1142/s0218127405012636.

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Cell differentiation is a complex process governed by the timely activation of genes resulting in a specific phenotype or observable physical change. Recent reports have indicated heterogeneity in gene expression even amongst identical colonies (clones). While some genes are always expressed, others are expressed with a finite probability. In this report, a mathematical framework is provided to understand the mechanism of osteoblast (bone forming cell) differentiation. A systematic approach using a combination of entropy, pair-wise dependency and Bayesian approach is used to gain insight into

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Grzegorcyzk, Marco, Dirk Husmeier, and Jörg Rahnenführer. "Modelling Nonstationary Gene Regulatory Processes." Advances in Bioinformatics 2010 (July 20, 2010): 1–17. http://dx.doi.org/10.1155/2010/749848.

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An important objective in systems biology is to infer gene regulatory networks from postgenomic data, and dynamic Bayesian networks have been widely applied as a popular tool to this end. The standard approach for nondiscretised data is restricted to a linear model and a homogeneous Markov chain. Recently, various generalisations based on changepoint processes and free allocation mixture models have been proposed. The former aim to relax the homogeneity assumption, whereas the latter are more flexible and, in principle, more adequate for modelling nonlinear processes. In our paper, we compare

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Bellucci, Gabriele. "A Model of Trust." Games 13, no. 3 (2022): 39. http://dx.doi.org/10.3390/g13030039.

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Trust is central to a large variety of social interactions. Different research fields have empirically and theoretically investigated trust, observing trusting behaviors in different situations and pinpointing their different components and constituents. However, a unifying, computational formalization of those diverse components and constituents of trust is still lacking. Previous work has mainly used computational models borrowed from other fields and developed for other purposes to explain trusting behaviors in empirical paradigms. Here, I computationally formalize verbal models of trust in

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Brugnetti, Ennio, Guido Coletta, Fabrizio De Caro, Alfredo Vaccaro, and Domenico Villacci. "Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts." Energies 13, no. 13 (2020): 3501. http://dx.doi.org/10.3390/en13133501.

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Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized a

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Liddell, Torrin M., and John K. Kruschke. "Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type." Judgment and Decision Making 9, no. 6 (2014): 523–47. http://dx.doi.org/10.1017/s1930297500006409.

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AbstractPunishment is an important method for discouraging uncooperative behavior. We use a novel design for a public goods game in which players have explicit intended contributions with accidentally changed actual contributions, and in which players can apply costly fines or ostracism. Moreover, all players except the subject are automated, whereby we control the intended contributions, actual contributions, costly fines, and ostracisms experienced by the subject. We assess subject’s utilization of other players’ intended and actual contributions when making decisions to fine or ostracize. H

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Safron, Adam. "The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again." Entropy 23, no. 6 (2021): 783. http://dx.doi.org/10.3390/e23060783.

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Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences prov

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Bihl, Trevor, Todd Jenkins, Chadwick Cox, Ashley DeMange, Kerry Hill, and Edmund Zelnio. "From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9635–43. http://dx.doi.org/10.1609/aaai.v33i01.33019635.

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As research and development (R&D) in autonomous systems progresses further, more interdisciplinary knowledge is needed from domains as diverse as artificial intelligence (AI), bi-ology, psychology, modeling and simulation (M&S), and robotics. Such R&D efforts are necessarily interdisciplinary in nature and require technical as well as further soft skills of teamwork, communication and integration. In this paper, we introduce a 14 week, summer long internship for developing these skills in undergraduate science and engineering interns through R&D. The internship was designed to

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Maxwell, Joshua, Lin Fang, and Joshua Carlson. "Do Carryover Effects Influence Attentional Bias to Threat in the Dot-Probe Task?" Journal of Trial and Error 2, no. 1 (2022): 70–76. http://dx.doi.org/10.36850/e9.

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Threatening stimuli are often thought to have sufficient potency to bias attention, relative to neutral stimuli. Researchers and clinicians opt for frequently used paradigms to measure such bias, such as the dot-probe task. Bias to threat in the dot-probe task is indicated by a congruency effect i.e., faster responses on congruent trials than incongruent trials (also referred to as attention capture). However, recent studies have found that such congruency effects are small and suffer from poor internal reliability. One explanation to low effect sizes and poor reliability is carryover effects

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McDonald, Kelsey R., John M. Pearson, and Scott A. Huettel. "Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior." Social Cognitive and Affective Neuroscience 15, no. 4 (2020): 383–93. http://dx.doi.org/10.1093/scan/nsaa053.

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Abstract Understanding how humans make competitive decisions in complex environments is a key goal of decision neuroscience. Typical experimental paradigms constrain behavioral complexity (e.g. choices in discrete-play games), and thus, the underlying neural mechanisms of dynamic social interactions remain incompletely understood. Here, we collected fMRI data while humans played a competitive real-time video game against both human and computer opponents, and then, we used Bayesian non-parametric methods to link behavior to neural mechanisms. Two key cognitive processes characterized behavior

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Stock, Ann-Kathrin, Annett Werner, Paul Kuntke, et al. "Gamma-Aminobutyric Acid and Glutamate Concentrations in the Striatum and Anterior Cingulate Cortex Not Found to Be Associated with Cognitive Flexibility." Brain Sciences 13, no. 8 (2023): 1192. http://dx.doi.org/10.3390/brainsci13081192.

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Behavioral flexibility and goal-directed behavior heavily depend on fronto-striatal networks. Within these circuits, gamma-aminobutyric acid (GABA) and glutamate play an important role in (motor) response inhibition, but it has remained largely unclear whether they are also relevant for cognitive inhibition. We hence investigated the functional role of these transmitters for cognitive inhibition during cognitive flexibility. Healthy young adults performed two paradigms assessing different aspects of cognitive flexibility. Magnetic resonance spectroscopy (MRS) was used to quantify GABA+ and tot

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Allen, Ronald J., and Michael S. Pardo. "Relative plausibility and its critics." International Journal of Evidence & Proof 23, no. 1-2 (2019): 5–59. http://dx.doi.org/10.1177/1365712718813781.

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Within legal scholarship there is a tendency to use (perhaps overuse) “paradigm shift” in ways far removed from the process famously described by Thomas Kuhn. Within the field of evidence, however, a phenomenon very similar to a paradigm shift, in the Kuhnian sense, is occurring. Although not on the scale of the transformation from Newtonian to Einsteinian physics or other tectonic shifts in science, the best understanding of juridical proof is shifting from probabilism to explanationism. For literally hundreds of years, proof at trial was assumed to be probabilistic. This assumption was given

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Longarzo, Mariachiara, Carlo Cavaliere, Giulia Mele, et al. "Microstructural Changes in Motor Functional Conversion Disorder: Multimodal Imaging Approach on a Case." Brain Sciences 10, no. 6 (2020): 385. http://dx.doi.org/10.3390/brainsci10060385.

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Background: Functional motor conversion disorders are characterized by neurological symptoms unrelated to brain structural lesions. The present study was conducted on a woman presenting motor symptoms causing motor dysfunction, using advanced multimodal neuroimaging techniques, electrophysiological and neuropsychological assessment. Methods. The patient underwent fluorodeoxyglucose-positron emission tomography-computed tomography (FDG-PET-CT) and functional magnetic resonance imaging (fMRI) with both task and resting-state paradigms and was compared with 11 healthy matched controls. To test di

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Bahnmueller, Julia, Krzysztof Cipora, Silke Melanie Göbel, Hans-Christoph Nuerk, and Mojtaba Soltanlou. "Pick the smaller number: No influence of linguistic markedness on three-digit number processing." Journal of Numerical Cognition 7, no. 3 (2021): 295–307. http://dx.doi.org/10.5964/jnc.6057.

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The symbolic number comparison task has been widely used to investigate the cognitive representation and underlying processes of multi-digit number processing. The standard procedure to establish numerical distance and compatibility effects in such number comparison paradigms usually entails asking participants to indicate the larger of two presented multi-digit Arabic numbers rather than to indicate the smaller number. In terms of linguistic markedness, this procedure includes the unmarked/base form in the task instruction (i.e., large). Here we evaluate distance and compatibility effects in

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