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Journal articles on the topic 'Bayesian regulation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zhao, Ying Ding, and Xian Xia Qiao. "Review of Modeling Methods of Gene Expression Regulation Networks." Applied Mechanics and Materials 433-435 (October 2013): 783–87. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.783.

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the study of Gene expression regulation network is expected to fully disclose function and behavior of the genome from the perspective of system. Now, Gene expression regulation network construction methods include Boolean networks, linear combination, the weighted matrix, Bayesian networks and differential equation, And so on, These methods are real abstraction of gene expression regulation network at different levels. Boolean network is qualitative research network of gene expression regulating, And Differential equation is quantified to describe biological processes by detailed mathematical

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Holmgren, Noél Michael André, Niclas Norrström, Robert Aps, and Sakari Kuikka. "A Concept of Bayesian Regulation in Fisheries Management." PLoS ONE 9, no. 11 (2014): e111614. http://dx.doi.org/10.1371/journal.pone.0111614.

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Muhammad, Afiq Wiratma, and Bhina Patria. "Self-Regulation Effects on Smoking Addiction: A Bayesian Approach." Universitas Psychologica 20 (December 15, 2021): 1–10. http://dx.doi.org/10.11144/javeriana.upsy20.sres.

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This study aimed to determine the impacts of self-regulation on smoking addiction. Self-regulation is the capacity to overcome ineffective behavior to achieve goals. Previous research studies found that self-regulation is an effective mental defense against deviant behaviors such as smoking. This study hypothesized that self-regulation correlates negatively with smoking addiction. The subjects in this study were 155 students aged 13-18 years (M = 13.37, SD = 1.07) from three high schools in Sleman district, Yogyakarta, Indonesia. The data were analysed with Bayesian regression and simple linea

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Sitanggang, Sahat Sonang, Sarjon Defit, and Mukhlis Ramadhan. "Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 7, no. 3 (2021): 359. http://dx.doi.org/10.26418/jp.v7i3.50092.

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Banyak metode fungsi pelatihan dalam Machine Learning Neural Network yang digunakan dalam menyelesaikan masalah komputasi yang berkaitan dengan prediksi. Fungsi pelatihan yang digunakan pada Machine Learning metoda algoritma backpropagation dapat menghasilkan prediksi yang berbeda, yang dipengaruhi oleh parameter dan data yang digunakan. Tujuan dari penelitian dilakukan untuk menganalisa performance dan keakuratan algoritma backpropagation standard serta mengoptimalkan fungsi pelatihan dengan algoritma Bayesian Regulation, dan One Step Secant. Dalam proses analisis, penelitian ini menggunakan

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Guo, Yingni, and Eran Shmaya. "Robust Monopoly Regulation." American Economic Review 115, no. 2 (2025): 599–634. https://doi.org/10.1257/aer.20191950.

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We study how to regulate a monopolistic firm using a robust-design, non-Bayesian approach. We derive a policy that minimizes the regulator’s worst-case regret, where regret is the difference between the regulator’s complete-information payoff and his realized payoff. When the regulator’s payoff is consumers’ surplus, he caps the firm’s average revenue. When his payoff is the total surplus of both consumers and the firm, he offers a piece rate subsidy to the firm while capping the total subsidy. For intermediate cases, the regulator combines these three policy instruments to balance three goals

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Sabatti, Chiara, and Gareth M. James. "Bayesian sparse hidden components analysis for transcription regulation networks." Bioinformatics 22, no. 6 (2005): 739–46. http://dx.doi.org/10.1093/bioinformatics/btk017.

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7

Li, Mingliang, Kevin J. Mumford, and Justin L. Tobias. "A Bayesian analysis of payday loans and their regulation." Journal of Econometrics 171, no. 2 (2012): 205–16. http://dx.doi.org/10.1016/j.jeconom.2012.06.010.

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Wanto, Anjar, Sarjon Defit, and Agus Perdana Windarto. "Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (2021): 254–64. http://dx.doi.org/10.29207/resti.v5i2.3031.

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Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disa

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9

Rodgers, W., and HO Tucker. "Neurological Mechanisms in Immune Regulation." International Journal Of Biology And Medicine 1, no. 1 (2019): 88–92. https://doi.org/10.36811/ijbm.2019.110011.

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This paper is a survey of the neurological mechanisms involved in the regulation of immune function. Its specific focus is to explore the anatomical regions of the brain that mediate inflammatory responses throughout the body. It begins by briefly reviewing experiments that elucidate a connection between psychological process and immune function. It also introduces the physiological connections that enable communication between the central nervous system and peripheral immune system. It then examines the brain regions involved in regulating immune responses, with additional insights drawn from

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Song, Lei, and Xiao Qing Hu. "Fitting the GPS/Leveling Quasi-Geoid Using Bayesian-Regulation BP Neural Network." Applied Mechanics and Materials 90-93 (September 2011): 2903–6. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2903.

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The 2.5′×2.5′resolution local quasi-geoid is calculated using the global gravity field model and GPS/leveling data of region which points spacing is about 10km with the Bayesian- regulation BP neural network in this paper. The inner and outer precision of quasi-geoid are both superior 0.05m.The result indicat that the Bayesian regulation BP neural network could improve the precision of fitting and restrain the over-fitting in fitting. The region quasi-geoid excelled than 0.05m can be computed using the global gravity field model and about 10km baseline GPS/leveling data in smoothness region.

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Aisyah, Siti, Zulkifli Zulkifli, and Pandu Adi Cakranegara. "Penerapan Algoritma Bayesian Regulation untuk Estimasi Posisi Cadangan Devisa Indonesia." Journal of Computer System and Informatics (JoSYC) 3, no. 4 (2022): 205–11. http://dx.doi.org/10.47065/josyc.v3i4.2170.

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Foreign exchange reserves need to be predicted because it is a significant monetary indicator to show the strength or weakness of a country's economic fundamentals. Therefore, the purpose of this study is to estimate the position of Indonesia's foreign exchange reserves at the end of 2022 and 2023 so that the government has benchmarks and information in determining the right economic policy so that the position of foreign exchange reserves remains stable. The estimation algorithm used in this study is the Bayesian Regulation algorithm, one of the Artificial Neural Network algorithms. The resea

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Tamminen, Katherine A., Patrick Gaudreau, Carolyn E. McEwen, and Peter R. E. Crocker. "Interpersonal Emotion Regulation Among Adolescent Athletes: A Bayesian Multilevel Model Predicting Sport Enjoyment and Commitment." Journal of Sport and Exercise Psychology 38, no. 6 (2016): 541–55. http://dx.doi.org/10.1123/jsep.2015-0189.

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Efforts to regulate emotions can influence others, and interpersonal emotion regulation within teams may affect athletes’ own affective and motivational outcomes. We examined adolescent athletes’ (N = 451, N teams = 38) self- and interpersonal emotion regulation, as well as associations with peer climate, sport enjoyment, and sport commitment within a multilevel model of emotion regulation in teams. Results of multilevel Bayesian structural equation modeling showed that athletes’ self-worsening emotion regulation strategies were negatively associated with enjoyment while other-improving emotio

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YUAN, AO, GUANJIE CHEN, and CHARLES ROTIMI. "GENETIC NETWORK ANALYSIS BY QUASI-BAYESIAN METHOD." Journal of Bioinformatics and Computational Biology 07, no. 01 (2009): 175–92. http://dx.doi.org/10.1142/s0219720009004059.

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Genetic network analysis provides an important statistical strategy for the study of gene–gene interactions. Although existing methods work well in practice, several opportunities for improvement remain. For example, the regulation coefficients of some of the existing methods are not easy to solve, nor are the solutions they provide unique. Also, as genetic network analysis are typically applied to small datasets with large number of parameters, having prior knowledge about the parameters is valuable and should be incorporated into the analysis. The uniqueness of the parameter estimate and com

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Ilinich, V. V., A. V. Perminov, and N. V. Gavrilovskaya. "Assessing Probability Characteristics of Reservoir Operation Using Bayesian Technology." Izvestiya of Altai State University, no. 1(129) (March 28, 2023): 95–99. http://dx.doi.org/10.14258/izvasu(2023)1-15.

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This study explores the use of Bayes' theorem to determine the probability characteristics of reservoir operation, specifically in cases where the reservoir capacity is exceeded or sustained within its active storage capacity. The investigation focuses on moments of streamflow increase during the spring flood and water shortage in the fall. The study aims to develop methods for regulating reservoir runoff using Bayesian technology, with specific tasks including developing a calculation algorithm to estimate the probabilistic characteristics of reservoir operation based on the Bayes formula wit

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15

Colditz, Ian G. "A consideration of physiological regulation from the perspective of Bayesian enactivism." Physiology & Behavior 214 (February 2020): 112758. http://dx.doi.org/10.1016/j.physbeh.2019.112758.

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Choi, Boseung, Yu-Yu Cheng, Selahattin Cinar, et al. "Bayesian inference of distributed time delay in transcriptional and translational regulation." Bioinformatics 36, no. 2 (2019): 586–93. http://dx.doi.org/10.1093/bioinformatics/btz574.

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Abstract Motivation Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challengin

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Lantz, Björn. "A non-Bayesian piecewise linear approximation adjustment process for incentive regulation." Information Economics and Policy 19, no. 1 (2007): 95–101. http://dx.doi.org/10.1016/j.infoecopol.2006.09.001.

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18

Tienda-Luna, Isabel M., Yufang Yin, Yufei Huang, Diego P. Ruiz Padillo, Maria C. Carrion Perez, and Yufeng Wang. "Constructing Gene Networks Using Variational Bayesian Variable Selection." Artificial Life 14, no. 1 (2008): 65–79. http://dx.doi.org/10.1162/artl.2008.14.1.65.

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We propose a Bayesian approach for constructing gene networks based on microarray data. Especially, we focus on Bayesian methods that can provide soft (probabilistic) information. This soft information is attractive not only for its ability to measure the level of confidence of the solution, but also because it can be used to realize Bayesian data integration, an extremely important task in gene network research. We propose a variable selection formulation of gene regulation and develop an inference solution based on a variational Bayesian expectation maximization (VBEM) learning rule. This so

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Chen, Yanlin, and Jun Zhang. "Signalling by Bayesian Persuasion and Pricing Strategy." Economic Journal 130, no. 628 (2020): 976–1007. http://dx.doi.org/10.1093/ej/ueaa002.

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Abstract This article investigates how a privately informed seller could signal her type through Bayesian persuasion and pricing strategy. We find that it is generally impossible to achieve separation through one channel alone. Furthermore, the outcome that survives the intuitive criterion always exists and is unique. This outcome is separating, for which a closed-form solution is provided. The signalling concern forces the high-type seller to disclose inefficiently more information and charge a higher price, resulting in fewer sales and lower profit. Finally, we show that a regulation on mini

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Mhamdi, Hanen, Jérémie Bourdon, Abdelhalim Larhlimi, and Mourad Elloumi. "Bayesian Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks." Informatics 7, no. 1 (2020): 1. http://dx.doi.org/10.3390/informatics7010001.

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The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance

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21

Chen, Ling Yun. "Study on Urban Residential Quality Engineering Based on Government Regulation." Applied Mechanics and Materials 484-485 (January 2014): 708–12. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.708.

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This paper describes the connotations and features of residential quality; according to the clues to the life cycle of real estate projects, based on the Static Bayesian theory in economic game theory, Backward Induction Method and Principal-Agent Theory, it analyzes three existing government regulation consisted of land transfer system, advance sale of commodity housing system and construction management system, and points out that the production problems of residential quality rooted in three irrational, imperfect government regulation. Then, it puts forward feasible proposals for the existi

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Hong, Liang, and Ryan Martin. "Real-time Bayesian non-parametric prediction of solvency risk." Annals of Actuarial Science 13, no. 1 (2018): 67–79. http://dx.doi.org/10.1017/s1748499518000039.

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AbstractInsurance regulation often dictates that insurers monitor their solvency risk in real time and take appropriate actions whenever the risk exceeds their tolerance level. Bayesian methods are appealing for prediction problems thanks to their ability to naturally incorporate both sample variability and parameter uncertainty into a predictive distribution. However, handling data arriving in real time requires a flexible non-parametric model, and the Monte Carlo methods necessary to evaluate the predictive distribution in such cases are not recursive and can be too expensive to rerun each t

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23

Sae Joo, Phongthanat, Charan Sanrach, and Sumalee Chaijaroen. "A Comparison of Classification Technique for Metacognitive Knowledge." Advanced Materials Research 403-408 (November 2011): 4538–42. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4538.

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Metcognitive learning has been developed to 1) enhance students to have awareness for conducting self study, 2) verify metacognitive knowledge and 3) provide proper lessons for each student. The test of metacognitive knowledge was implemented, and at least two out of three metacognitive knowledges; knowledge of self, knowledge of task, and knowledge of strategy, should be presented so that students’ metacognitive regulation can be proved. Therefore classification techniques were proposed to classify metacognitive knowledge of students via accuracy comparison of four classification techniques:

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Liu, Guanghui, Xiaohui Wang, Yuebo Meng, Yalin Zhang, and Tingting Chen. "Research on Prediction and Regulation of Thermal Dissatisfaction Rate Based on Personalized Differences." Applied Sciences 13, no. 13 (2023): 7978. http://dx.doi.org/10.3390/app13137978.

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Thermal discomfort body language has been shown to be a psychological representation of personnel’s particular thermal comfort. Individual thermal comfort differences are ignored in public building settings with random personnel flow. To solve this issue, we suggested a Bayesian group thermal dissatisfaction rate prediction model based on thermal discomfort body language expression and subsequently implemented intelligent indoor temperature and humidity control. The PMV-PPD model was utilized to represent the group’s overall thermal comfort and to create a prior distribution of thermal dissati

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Firzada, Fahmi, and Surya Darma. "Algoritma Bayesian Regulation untuk Prediksi Kemiskinan Sebagai Evaluasi Awal Mendukung Kebijakan Ekonomi Hijau." Journal of Computer System and Informatics (JoSYC) 6, no. 1 (2024): 363–73. https://doi.org/10.47065/josyc.v6i1.6011.

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This study aims to utilize the Bayesian Regulation algorithm to predict poverty in Simalungun, Pematangsiantar, Asahan, Batu Bara, and Tebing Tinggi, as an initial step to evaluate the Green Economy policy. Poverty remains a serious issue, particularly in Pematangsiantar and Simalungun, where social inequality and limited access to basic services are prevalent. High poverty rates and limited resources present significant challenges to improving community welfare. The Green Economy policy could be a potential solution to reduce the negative environmental impact of development and enhance commun

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Kikuchi, Hiroaki, Hyun Jun Jung, Viswanathan Raghuram, et al. "Bayesian identification of candidate transcription factors for the regulation of Aqp2 gene expression." American Journal of Physiology-Renal Physiology 321, no. 3 (2021): F389—F401. http://dx.doi.org/10.1152/ajprenal.00204.2021.

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Abetted by the advent of systems biology-based (“-omics”) techniques in the 21st century, there has been a massive expansion of published data relevant to virtually every physiological question. The authors have developed a large-scale data integration approach based on the application of Bayes’' theorem. In the current work, they integrated 12 different -omics data sets to identify the transcription factors most likely to mediate vasopressin-dependent regulation of transcription of the aquaporin-2 gene.

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Bulashevska, Svetlana, Ezekiel Adebiyi, Benedikt Brors, and Roland Eils. "New Insights into the Genetic Regulation of Plasmodium Falciparum Obtained by Bayesian Modeling." Gene Regulation and Systems Biology 1 (January 2007): 117762500700100. http://dx.doi.org/10.1177/117762500700100013.

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Accoto, Nadia, Tobias Ryden, and Piercesare Secchi. "Bayesian Hidden Markov Models for Performance-Based Regulation of Continuity of Electricity Supply." IEEE Transactions on Power Delivery 25, no. 3 (2010): 1236–49. http://dx.doi.org/10.1109/tpwrd.2010.2047514.

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Heron, Elizabeth A., Bärbel Finkenstädt, and David A. Rand. "Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study." Bioinformatics 23, no. 19 (2007): 2596–603. http://dx.doi.org/10.1093/bioinformatics/btm367.

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Conlon, Erin M., Bradley L. Postier, Barbara A. Methé, Kelly P. Nevin, and Derek R. Lovley. "A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information." PLoS ONE 7, no. 12 (2012): e52137. http://dx.doi.org/10.1371/journal.pone.0052137.

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Oye, K. A. "The precautionary principle and international conflict over domestic regulation: mitigating uncertainty and improving adaptive capacity." Water Science and Technology 52, no. 6 (2005): 59–64. http://dx.doi.org/10.2166/wst.2005.0151.

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Disputes over invocation of precaution in the presence of uncertainty are building. This essay finds: (1) analysis of past WTO panel decisions and current EU-US regulatory conflicts suggests that appeals to scientific risk assessment will not resolve emerging conflicts; (2) Bayesian updating strategies, with commitments to modify policies as information emerges, may ameliorate conflicts over precaution in environmental and security affairs.

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Tawfiq, Luma N. M., and Othman M. Salih. "Using Feed Forward Neural Network to Solve Eigenvalue Problems." Conference Papers in Science 2014 (March 31, 2014): 1–8. http://dx.doi.org/10.1155/2014/906376.

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The aim of this paper is to presents a parallel processor technique for solving eigenvalue problem for ordinary differential equations using artificial neural networks. The proposed network is trained by back propagation with different training algorithms quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. The next objective of this paper was to compare the performance of aforementioned algorithms with regard to predicting ability.

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De Backer, Loan, Wouter Schelfhout, Mathea Simons, Ellen Vandervieren, and Jose Rivera Espejo. "Impact of Team Teaching on Student Teachers’ Professional Identity: A Bayesian Approach." Education Sciences 13, no. 11 (2023): 1087. http://dx.doi.org/10.3390/educsci13111087.

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Workplace learning in teacher education is essential for creating and recreating the professional identity of student teachers. Innovative interventions, such as team teaching between student teachers and mentors at the workplace, are assumed to facilitate learning to teach. This experimental study provides valuable insight into the impact of team teaching on student teachers’ professional identity by implementing distinct student teaching formats: team teaching (A1 intervention), team teaching with support (A2 intervention), and traditional teaching (Control intervention). In this study, prof

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Penfold, Christopher A., Vicky Buchanan-Wollaston, Katherine J. Denby, and David L. Wild. "Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks." Bioinformatics 28, no. 12 (2012): i233—i241. http://dx.doi.org/10.1093/bioinformatics/bts222.

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Abstract Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number

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Sabourin, Jennifer L., Jonathan P. Rowe, Bradford W. Mott, and James C. Lester. "Considering Alternate Futures to Classify Off-Task Behavior as Emotion Self-Regulation: A Supervised Learning Approach." Journal of Educational Data Mining 5, no. 1 (2013): 9–38. https://doi.org/10.5281/zenodo.3554608.

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Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as offtask behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we investigate the affective role of off-task behavior by analyzing data from student interactions with CRYSTAL ISLAND, a narrative-centered learning environment for middle school microbiology. We observe that off-task behavior is associated with reduced

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Barua, Arnab, and Haralampos Hatzikirou. "Cell Decision Making through the Lens of Bayesian Learning." Entropy 25, no. 4 (2023): 609. http://dx.doi.org/10.3390/e25040609.

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Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker–Planck equation for cell-microenvi

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Ptak-Chmielewska, Aneta, and Paweł Kopciuszewski. "New Definition of Default—Recalibration of Credit Risk Models Using Bayesian Approach." Risks 10, no. 1 (2022): 16. http://dx.doi.org/10.3390/risks10010016.

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After the financial crisis, the European Banking Authority (EBA) has established tighter standards around the definition of default (Capital Requirements Regulation CRR Article 178, EBA/GL/2017/16) to increase the degree of comparability and consistency in credit risk measurement and capital frameworks across banks and financial institutions. Requirements of the new definition of default (DoD) concern how banks recognize credit defaults for prudential purposes and include quantitative impact analysis and new rules of materiality. In this approach, the number and timing of defaults affect the v

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Kurniadi, Rizal. "Comparative Evaluation on Nuclear Shell Correction Calculation using Regulation Bayesian and Moving Average Methods." Journal of Physics: Conference Series 2243, no. 1 (2022): 012060. http://dx.doi.org/10.1088/1742-6596/2243/1/012060.

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Abstract Since Strutinsky had proposed a shell correction for nuclear mass calculations in 1967, there has been much further research on this topic. In addition to the development carried out on the Strutinsky method, some studies try to apply the shell correction. This paper is included in the development of the shell correction method. There are two techniques used in this work the first is the average method, while the second is the Bayesian regulation method. The two techniques are very different from those Strutinsky originally developed. He used the hermit polynomial as an extension of d

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Lin, Kuang, and Dirk Husmeier. "Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization." EURASIP Journal on Bioinformatics and Systems Biology 2009, no. 1 (2009): 601068. http://dx.doi.org/10.1186/1687-4153-2009-601068.

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Lin, Kuang, and Dirk Husmeier. "Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization." EURASIP Journal on Bioinformatics and Systems Biology 2009 (2009): 1–26. http://dx.doi.org/10.1155/2009/601068.

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41

Liu, Jun S. "The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem." Journal of the American Statistical Association 89, no. 427 (1994): 958–66. http://dx.doi.org/10.1080/01621459.1994.10476829.

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Puspadini, Ratih, Anjar Wanto, and Nur Arminarahmah. "Penerapan ML dengan Teknik Bayesian Regulation untuk Peramalan Usia Penduduk di Beberapa Negara Asia." Journal of Computer System and Informatics (JoSYC) 3, no. 3 (2022): 147–55. http://dx.doi.org/10.47065/josyc.v3i3.1692.

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Knowing the age of life of the population in a country is useful for evaluating the performance of the government, whether the government is able to prosper the population in general, and improve health status in particular. The purpose of this paper is to forecast the age of the population in several major countries in Asia, so that the government has a benchmark in determining policies to further improve the welfare and health of the population in their respective countries. The forecasting method in this paper will use Machine learning algorithms with Bayesian Regulation techniques. The res

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Costa, Marcelo Azevedo, Leandro Brioschi Mineti, Vinícius Diniz Mayrink, and Ana Lúcia Miranda Lopes. "Bayesian detection of clusters in efficiency score maps: An application to Brazilian energy regulation." Applied Mathematical Modelling 68 (April 2019): 66–81. http://dx.doi.org/10.1016/j.apm.2018.11.009.

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Claxton, Karl. "Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals." Health Economics 8, no. 3 (1999): 269–74. http://dx.doi.org/10.1002/(sici)1099-1050(199905)8:3<269::aid-hec425>3.0.co;2-d.

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Rhodes, Benjamin, David L. Morris, Lakshman Subrahmanyan, et al. "Fine-mapping the genetic basis of CRP regulation in African Americans: a Bayesian approach." Human Genetics 123, no. 6 (2008): 633–42. http://dx.doi.org/10.1007/s00439-008-0517-5.

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Kim, Yongku, and Jeongjin Lee. "A Risk Assessment for Ozone Regulation Based on Statistical Rollback." Applied Sciences 11, no. 5 (2021): 2388. http://dx.doi.org/10.3390/app11052388.

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In environmental studies, it is important to assess how regulatory standards for air pollutants affect public health. High ozone levels contribute to harmful air pollutants. The EPA regulates ozone levels by setting ozone standards to protect public health. It is thus crucial to assess how various regulatory ozone standards affect non-accidental mortality related to respiratory deaths during the ozone season. The original rollback approach provides an adjusted ozone process under a new regulation scenario in a deterministic fashion. Herein, we consider a statistical rollback approach to allow

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Lomeli, Luis Martinez, Abdon Iniguez, Prasanthi Tata, et al. "Optimal experimental design for mathematical models of haematopoiesis." Journal of The Royal Society Interface 18, no. 174 (2021): 20200729. http://dx.doi.org/10.1098/rsif.2020.0729.

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The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framew

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Hashim, Mimi Nurzilah, Muhammad Khusairi Osman, Mohammad Nizam Ibrahim, Ahmad Farid Abidin, and Ahmad Asri Abd Samat. "A Comparison Study of Learning Algorithms for Estimating Fault Location." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 2 (2017): 464. http://dx.doi.org/10.11591/ijeecs.v6.i2.pp464-472.

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Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current sign

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Shah, Rohan N., and Alexander J. Ruthenburg. "Sequence deeper without sequencing more: Bayesian resolution of ambiguously mapped reads." PLOS Computational Biology 17, no. 4 (2021): e1008926. http://dx.doi.org/10.1371/journal.pcbi.1008926.

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Next-generation sequencing (NGS) has transformed molecular biology and contributed to many seminal insights into genomic regulation and function. Apart from whole-genome sequencing, an NGS workflow involves alignment of the sequencing reads to the genome of study, after which the resulting alignments can be used for downstream analyses. However, alignment is complicated by the repetitive sequences; many reads align to more than one genomic locus, with 15–30% of the genome not being uniquely mappable by short-read NGS. This problem is typically addressed by discarding reads that do not uniquely

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Anh, Dao Nam, Bui Duong Hung, Pham Quang Huy, and Dang Xuan Tho. "Feature Analysis for Imbalanced Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 5 (2020): 648–55. http://dx.doi.org/10.20965/jaciii.2020.p0648.

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Based on the results of artificial samples generated in the minority class and through the label regulation of the neighbor samples of the majority class, the precision of the classification prediction for imbalanced learning has clearly been enhanced. This article presents a unified solution combining learning factors to improve the learning performance. The proposed method solves this imbalance through a feature selection incorporating the generation of artificial samples and label regulation. A probabilistic representation is used for all aspects of learning: class, sample, and feature. A B

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