Journal articles: 'Bayes Modelling'

1

Canale, A., and D. B. Dunson. "Nonparametric Bayes modelling of count processes." Biometrika 100, no. 4 (October 5, 2013): 801–16. http://dx.doi.org/10.1093/biomet/ast037.

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Schulte, Oliver, Hassan Khosravi, Arthur E. Kirkpatrick, Tianxiang Gao, and Yuke Zhu. "Modelling relational statistics with Bayes Nets." Machine Learning 94, no. 1 (May 1, 2013): 105–25. http://dx.doi.org/10.1007/s10994-013-5362-7.

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Verrall, R. J. "Bayes and Empirical Bayes Estimation for the Chain Ladder Model." ASTIN Bulletin 20, no. 2 (November 1990): 217–43. http://dx.doi.org/10.2143/ast.20.2.2005444.

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AbstractThe subject of predicting outstanding claims on a porfolio of general insurance policies is approached via the theory of hierarchical Bayesian linear models. This is particularly appropriate since the chain ladder technique can be expressed in the form of a linear model. The statistical methods which are applied allow the practitioner to use different modelling assumptions from those implied by a classical formulation, and to arrive at forecasts which have a greater degree of inherent stability. The results can also be used for other linear models. By using a statistical structure, a sound approach to the chain ladder technique can be derived. The Bayesian results allow the input of collateral information in a formal manner. Empirical Bayes results are derived which can be interpreted as credibility estimates. The statistical assumptions which are made in the modelling procedure are clearly set out and can be tested by the practitioner. The results based on the statistical theory form one part of the reserving procedure, and should be followed by expert interpretation and analysis. An illustration of the use of Bayesian and empirical Bayes estimation methods is given.

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Wibawa, Aji Prasetya, Yana Ningtyas, Nimas Hadi Atmaja, Ilham Ari Elbaith Zaeni, Agung Bella Putra Utama, Felix Andika Dwiyanto, and Andrew Nafalski. "Modelling Naïve Bayes for Tembang Macapat Classification." Harmonia: Journal of Arts Research and Education 22, no. 1 (July 1, 2022): 28–36. http://dx.doi.org/10.15294/harmonia.v22i1.34776.

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The tembang macapat can be classified using its cultural concepts of guru lagu, guru wilangan, and guru gatra. People may face difficulties recognizing certain songs based on the established rules. This study aims to build classification models of tembang macapat using a simple yet powerful Naïve Bayes classifier. The Naive Bayes can generate high-accuracy values from sparse data. This study modifies the concept of Guru Lagu by retrieving the last vowel of each line. At the same time, guru wilangan’s guidelines are amended by counting the number of all characters (Model 2) rather than calculating the number of syllables (Model 1). The data source is serat wulangreh with 11 types of tembang macapat, namely maskumambang, mijil, sinom, durma, asmaradana, kinanthi, pucung, gambuh, pangkur, dandhanggula, and megatruh. The k-fold cross-validation is used to evaluate the performance of 88 data. The result shows that the proposed Model 1 performs better than Model 2 in macapat classification. This promising method opens the potential of using a data mining classification engine as cultural teaching and preservation media.

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Boškoski, Pavle, Matija Perne, Martina Rameša, and Biljana Mileva Boshkoska. "Variational Bayes survival analysis for unemployment modelling." Knowledge-Based Systems 229 (October 2021): 107335. http://dx.doi.org/10.1016/j.knosys.2021.107335.

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Durante, D., and D. B. Dunson. "Nonparametric Bayes dynamic modelling of relational data." Biometrika 101, no. 4 (October 8, 2014): 883–98. http://dx.doi.org/10.1093/biomet/asu040.

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Shimoda, Yuko, and George B. Arhonditsis. "Integrating hierarchical Bayes with phosphorus loading modelling." Ecological Informatics 29 (September 2015): 77–91. http://dx.doi.org/10.1016/j.ecoinf.2015.07.005.

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Maes, M. A., M. R. Dann, and A. K. Midtgaard. "Spatial Hazard Rate Modelling Using Hierarchical Bayes Methods." Australian Journal of Structural Engineering 9, no. 1 (January 2009): 45–54. http://dx.doi.org/10.1080/13287982.2009.11465009.

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Lange, Nicholas. "Graphs and stochastic relaxation for hierarchical bayes modelling." Statistics in Medicine 11, no. 14-15 (1992): 2001–16. http://dx.doi.org/10.1002/sim.4780111417.

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Haidar, Ahmad, Elizabeth Potocka, Benoit Boulet, A. Margot Umpleby, and Roman Hovorka. "Estimating postprandial glucose fluxes using hierarchical Bayes modelling." Computer Methods and Programs in Biomedicine 108, no. 1 (October 2012): 102–12. http://dx.doi.org/10.1016/j.cmpb.2012.01.010.

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Rukhin, Andrew L. "Bayes and Empirical Bayes Methods for Data Analysis." Technometrics 39, no. 3 (August 1997): 337. http://dx.doi.org/10.1080/00401706.1997.10485131.

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Karimnezhad, Ali, and Fahimeh Moradi. "Bayes, E-Bayes and robust Bayes prediction of a future observation under precautionary prediction loss functions with applications." Applied Mathematical Modelling 40, no. 15-16 (August 2016): 7051–61. http://dx.doi.org/10.1016/j.apm.2016.02.040.

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Haas, A., and C. Jaeger. "Agents, Bayes, and Climatic Risks - a modular modelling approach." Advances in Geosciences 4 (August 9, 2005): 3–7. http://dx.doi.org/10.5194/adgeo-4-3-2005.

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Abstract. When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

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Colombo, Matteo, and Peggy Seriès. "Bayes in the Brain—On Bayesian Modelling in Neuroscience." British Journal for the Philosophy of Science 63, no. 3 (September 1, 2012): 697–723. http://dx.doi.org/10.1093/bjps/axr043.

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Bayoud, Husam Awni. "Bayes and Empirical Bayes Estimation of the Parameternin a Binomial Distribution." Communications in Statistics - Simulation and Computation 40, no. 9 (October 2011): 1422–33. http://dx.doi.org/10.1080/03610918.2010.526737.

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LANCONELLI, ALBERTO. "BAYES' FORMULA FOR SECOND QUANTIZATION OPERATORS." Stochastics and Dynamics 06, no. 02 (June 2006): 245–53. http://dx.doi.org/10.1142/s0219493706001748.

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The Bayes' formula provides the relationship between conditional expectations with respect to absolutely continuous measures. The conditional expectation is in the context of the Wiener space — an example of second quantization operator. In this note we obtain a formula that generalizes the above-mentioned Bayes' rule to general second quantization operators.

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Ekanayake, Jayalath Bandara. "Predicting Bug Priority Using Topic Modelling in Imbalanced Learning Environments." International Journal of Systems and Service-Oriented Engineering 11, no. 1 (January 2021): 31–42. http://dx.doi.org/10.4018/ijssoe.2021010103.

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Manual classification of bug reports is time-consuming as the reports are received in large quantities. Alternatively, this project proposed automatic bug prediction models to classify the bug reports. The topics or the candidate keywords are mined from the developer description in bug reports using RAKE algorithm and converted into attributes. These attributes together with the target attribute—priority level—construct the training datasets. Naïve Bayes, logistic regression, and decision tree learner algorithms are trained, and the prediction quality was measured using area under recursive operative characteristics curves (AUC) as AUC does not consider the biasness in datasets. The logistics regression model outperforms the other two models providing the accuracy of 0.86 AUC whereas the naïve Bayes and the decision tree learner recorded 0.79 AUC and 0.81 AUC, respectively. The bugs can be classified without developer involvement and logistic regression is also a potential candidate as naïve Bayes for bug classification.

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Woolrich, Mark W., Timothy E. J. Behrens, and Stephen M. Smith. "Constrained linear basis sets for HRF modelling using Variational Bayes." NeuroImage 21, no. 4 (April 2004): 1748–61. http://dx.doi.org/10.1016/j.neuroimage.2003.12.024.

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De Sousa Ribeiro, Fabio, Georgios Leontidis, and Stefanos Kollias. "Capsule Routing via Variational Bayes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3749–56. http://dx.doi.org/10.1609/aaai.v34i04.5785.

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Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are replaced with capsule vectors or matrices, whose entries represent different properties of objects. The relationships between objects and their parts are learned via trainable viewpoint-invariant transformation matrices, and the presence of a given object is decided by the level of agreement among votes from its parts. This interaction occurs between capsule layers and is a process called routing-by-agreement. In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE. Our Bayesian approach addresses some of the inherent weaknesses of MLE based models such as the variance-collapse by modelling uncertainty over capsule pose parameters. We outperform the state-of-the-art on smallNORB using ≃50% fewer capsules than previously reported, achieve competitive performances on CIFAR-10, Fashion-MNIST, SVHN, and demonstrate significant improvement in MNIST to affNIST generalisation over previous works.1

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Natarajan, Kannan, Malay Ghosh, and Tapabrata Maiti. "Hierarchical bayes quality measurement plan." Communications in Statistics - Simulation and Computation 27, no. 1 (January 1998): 199–214. http://dx.doi.org/10.1080/03610919808813475.

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Candelieri, Antonio, Andrea Ponti, Ilaria Giordani, and Francesco Archetti. "Lost in Optimization of Water Distribution Systems: Better Call Bayes." Water 14, no. 5 (March 3, 2022): 800. http://dx.doi.org/10.3390/w14050800.

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The main goal of this paper is to show that Bayesian optimization can be regarded as a general framework for the data-driven modelling and solution of problems arising in water distribution systems. Scenario-based hydraulic simulation and Monte Carlo are key tools in modelling in water distribution systems. The related optimization problems fall into a simulation/optimization framework in which objectives and constraints are often black box. Bayesian optimization (BO) is characterized by a surrogate model, usually a Gaussian process but also a random forest, as well as neural networks and an acquisition function that drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible, and sample efficient, making it particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given, for instance, by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection of contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes, showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms are proposed for multi-objective detection problems using two different acquisition functions.

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O'Hagan, A., E. B. Glennie, and R. E. Beardsall. "Subjective Modelling and Bayes Linear Estimation in the UK Water Industry." Applied Statistics 41, no. 3 (1992): 563. http://dx.doi.org/10.2307/2348090.

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Sihwi, S. W., A. N. Fadhilah, M. P. Puspasari, and Winarno. "Recommendation System for Complementary Breastfeeding using Ontology Modelling and Naïve Bayes." Journal of Physics: Conference Series 1201 (May 2019): 012029. http://dx.doi.org/10.1088/1742-6596/1201/1/012029.

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Martinsek, Adam T. "Empirical bayes methods in sequential estimation." Sequential Analysis 6, no. 2 (January 1987): 119–37. http://dx.doi.org/10.1080/07474948708836120.

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Karunamuni, Rohana J. "An empirical bayes adaptive price search∗." Sequential Analysis 18, no. 3-4 (January 1999): 233–48. http://dx.doi.org/10.1080/07474949908836434.

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Hangos, Katalin M., László Leisztner, and Miroslav Kárný. "Calibration and measurement control based on Bayes statistics." Journal of Automatic Chemistry 11, no. 4 (1989): 149–55. http://dx.doi.org/10.1155/s1463924689000325.

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The Bayesian methodology described in this paper has the inherent capability of choosing, from calibration-type curves, candidates which are plausible with respect to measured data, expert knowledge and theoretical models (including the nature of the measurement errors). The basic steps of Bayesian calibration are reviewed and possible applications of the results are described in this paper. A calibration related to head-space gas chromatographic data is used as an example of the proposed method. The linear calibration case has been treated with a log-normal distributed measurement error. Such a treatment of noise stresses the importance of modelling the random constituents of any problem.

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Putu, Ni Luh Putu Merawati, Ahmad Zuli Amrullah, and Ismarmiaty. "Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 20, 2021): 123–31. http://dx.doi.org/10.29207/resti.v5i1.2587.

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Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.

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Szpak, Dawid, and Barbara Tchórzewska-Cieślak. "Modelling of failure rate of water supply network using the Bayes theorem." E3S Web of Conferences 44 (2018): 00175. http://dx.doi.org/10.1051/e3sconf/20184400175.

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The subject of the publication is the analysis and assessment of failure rate of the water supply network in a district city located in south-eastern Poland. The analysis was based on the failure rate index. In addition, the paper uses the Bayes theorem to determine the probability of failure of water supply network. The exploitation data obtained from the water supply company were used in the work. It was found that the water supply network of the analysed city is characterized by good technical condition.

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Sheikhian, H., M. R. Delavar, and A. Stein. "Predictive Modelling of Seismic Hazard Applying Naïve Bayes and Granular Computing Classifiers." Procedia Environmental Sciences 26 (2015): 49–52. http://dx.doi.org/10.1016/j.proenv.2015.05.022.

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Durgalakshmi, B., and V. Vijayakumar. "Progonosis and Modelling of Breast Cancer and its Growth Novel Naive Bayes." Procedia Computer Science 50 (2015): 551–53. http://dx.doi.org/10.1016/j.procs.2015.04.102.

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Hauer, Ezra. "Overdispersion in modelling accidents on road sections and in Empirical Bayes estimation." Accident Analysis & Prevention 33, no. 6 (November 2001): 799–808. http://dx.doi.org/10.1016/s0001-4575(00)00094-4.

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Karunamuni, R. J. "Empirical bayes sequential estimation of the mean." Sequential Analysis 11, no. 1 (January 1992): 37–53. http://dx.doi.org/10.1080/07474949208836243.

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Ghosh, Malay, and Robert M. Hoekstra. "A.P.O. Rules in hierarchical bayes regression models." Sequential Analysis 14, no. 2 (January 1995): 99–115. http://dx.doi.org/10.1080/07474949508836323.

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Karunamuni, R. J., and N. G. N. Prasad. "Empirical Bayes Sequential Estimation of Binomial Probabilities." Communications in Statistics - Simulation and Computation 32, no. 1 (January 4, 2003): 61–71. http://dx.doi.org/10.1081/sac-120013111.

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Kanika and Somesh Kumar. "Bayes estimation of a langevin mean direction." Communications in Statistics - Simulation and Computation 46, no. 4 (December 18, 2016): 2769–83. http://dx.doi.org/10.1080/03610918.2015.1062099.

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Gunel, Erdogan, and Kenneth Joseph Ryan. "Fisher's exact test from a Bayes perspective." Communications in Statistics - Simulation and Computation 46, no. 9 (April 27, 2017): 7393–404. http://dx.doi.org/10.1080/03610918.2016.1241402.

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Singpurwalla, Nozer D. "Decelerated Testing: A Hierarchical Bayes Approach." Technometrics 47, no. 4 (November 2005): 468–77. http://dx.doi.org/10.1198/004017005000000175.

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Lurie, Anna M., and Nagaraj K. Neerchal. "Bayes-type tests for constancy of autoregressive parameters." Environmetrics 10, no. 6 (November 1999): 737–52. http://dx.doi.org/10.1002/(sici)1099-095x(199911/12)10:6<737::aid-env386>3.0.co;2-g.

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Devine, Owen J., Thomas A. Louis, and M. Elizabeth Halloran. "Empirical bayes estimators for spatially correlated incidence rates." Environmetrics 5, no. 4 (December 1994): 381–98. http://dx.doi.org/10.1002/env.3170050403.

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Solow, Andrew R., and Arthur G. Gaines. "An empirical bayes approach to monitoring water quality." Environmetrics 6, no. 1 (January 1995): 1–5. http://dx.doi.org/10.1002/env.3170060102.

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Ugarte, M. D., A. F. Militino, and T. Goicoa. "Prediction error estimators in Empirical Bayes disease mapping." Environmetrics 19, no. 3 (2008): 287–300. http://dx.doi.org/10.1002/env.874.

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42

Karman, Joni, and Julius Saputra. "PERANCANGAN SISTEM PAKAR DIAGNOSA KERUSAKAN SEPEDA MOTOR HONDA BEBEK BERKARBURATOR DENGAN MENGGUNAKAN METODE TEOREMA BAYES BERBASIS WEB MOBILE." Jurnal Teknik Informatika Musirawas (JUTIM) 3, no. 1 (June 21, 2018): 58. http://dx.doi.org/10.32767/jutim.v3i1.304.

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AbstrakDalam penulisan penelitian ini penulis akan menjelaskan tentang pembuatan program sistem pakar diagnosa kerusakan pada sepeda motor honda bebek berkarburator dengan menggunakan metode teorema bayes berbasis web Mobile. Dengan bahasa pemrograman php. Kebutuhan-kebutuhan yang diperlukan dalam membangun suatu perangkat yaitu menggunakan UML (Unified modelling language). Database yang digunakan MySQL dan dikembangkan menggunakan sublime text. Hasil pengembangan meliputi data kerusakan, data gejala, data basis aturan, data solusi, data user dan data admin. Berdasarkan penelitian tersebut dapat disimpulkan bahwa perangkat lunak yang dibangun adalah perancangan sistem pakar diagnosa kerusakan pada sepeda motor honda bebek berkarburator dengan menggunakan metode teorema bayes berbasis web Mobile. Hasil dari perangkat lunak ini diharapkan mampu memberikan kemudahan dalam mendiagnosa kerusakan pada sepeda motor dalam memproses data menjadi informasi cepat, tepat dan akurat. Kata Kunci : Sistem pakar, Teorema Bayes, Web Mobile

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Brown, Lawrence D. "In-season prediction of batting averages: A field test of empirical Bayes and Bayes methodologies." Annals of Applied Statistics 2, no. 1 (March 2008): 113–52. http://dx.doi.org/10.1214/07-aoas138.

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Ghosh, Malay, and Bhramar Mukherjee. "Nonparametric Sequential Bayes Estimation of the Distribution Function." Sequential Analysis 24, no. 4 (October 2005): 389–409. http://dx.doi.org/10.1080/07474940500311013.

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Diebolt, Jean, Mhamed-Ali El-Aroui, Myriam Garrido, and Stéphane Girard. "Quasi-Conjugate Bayes Estimates for GPD Parameters and Application to Heavy Tails Modelling." Extremes 8, no. 1-2 (June 2005): 57–78. http://dx.doi.org/10.1007/s10687-005-4860-9.

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McKeague, I. W., and M. Tighiouart. "Nonparametric Bayes estimators for hazard functions based on right censored data." Tamkang Journal of Mathematics 33, no. 2 (June 30, 2002): 173–90. http://dx.doi.org/10.5556/j.tkjm.33.2002.297.

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In this article, we analyse right censored survival data by modelling their common hazard function nonparametrically. The hazard rate is assumed to be a stochastic process, with sample paths taking the form of step functions. This process jumps at times that form a time-homogeneous Poisson process, and a class of Markov random fields is used to model the values of these sample paths. Features of the posterior distribution, such as the mean hazard rate and survival probabilities, are evaluated using the Metropolis--Hastings--Green algorithm. We illustrate our methodology by simulation examples.

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Romanowski, Andrzej, Krzysztof Grudzień, Hela Garbaa, and Lidia Jackowska-Strumiłło. "PARAMETRIC METHODS FOR ECT INVERSE PROBLEM SOLUTION IN SOLID FLOW MONITORING." Informatics Control Measurement in Economy and Environment Protection 7, no. 1 (March 30, 2017): 50–54. http://dx.doi.org/10.5604/01.3001.0010.4582.

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The article presents the parametrisation-based methods of monitoring of the process of gravitational silo discharging with aid of capacitance tomography techniques. Proposed methods cover probabilistic Bayes’ modelling, including spatial and temporal analysis and Markov chain Monte Carlo methods as well as process parametrisation with artificial neural networks. In contrast to classical image reconstruction-based methods, parametric modelling allows to omit this stage as well as abandon the associated reconstruction errors. Parametric modelling enables the direct analysis of significant parameters of investigated process that in turn results in easier incorporation into the control feedback loop. Presented examples are given for the gravitational flow of bulk solids in silos.

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Duong, Q. P., and R. W. Shorrock. "An empirical bayes approach to the Behrens-Fisher problem." Environmetrics 3, no. 2 (1992): 183–92. http://dx.doi.org/10.1002/env.3170030204.

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Li, Naiyi, Yuan Li, Yongming Li, and Yang Liu. "Empirical Bayes Inference for the Parameter of Power Distribution Based on Ranked Set Sampling." Discrete Dynamics in Nature and Society 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/760768.

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Prakash, Gyan, and B. Prasad. "Bayes prediction intervals for the Rayleigh model." Model Assisted Statistics and Applications 5, no. 1 (February 11, 2010): 43–49. http://dx.doi.org/10.3233/mas-2010-0128.

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Journal articles on the topic 'Bayes Modelling'

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