Literatura académica sobre el tema "Bayes predictor"

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Artículos de revistas sobre el tema "Bayes predictor"

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Liang, Guohua, Xingquan Zhu, and Chengqi Zhang. "An Empirical Study of Bagging Predictors for Different Learning Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1802–3. http://dx.doi.org/10.1609/aaai.v25i1.8026.

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Bagging is a simple yet effective design which combines multiple single learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive emp
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Zhang, Shenghan, Yufeng Gu, Yinshan Gao, Xinxing Wang, Daoyong Zhang, and Liming Zhou. "Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs." Geofluids 2022 (October 5, 2022): 1–31. http://dx.doi.org/10.1155/2022/9443955.

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From a general review, most petrophysical models applied for the conventional logging interpretation imply that porosity, permeability, or water saturation mathematically have a linear or nonlinear relationship with well logs, and then arguing the prediction of these three parameters actually is accessible under a regression of logging sequences. Based on this knowledge, ensemble learning technique, partially developed for fitting problems, can be regarded as a solution. Light gradient boosting machine (LightGBM) is proved as one representative of the state-of-the-art ensemble learning, thus a
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Irmayani, Irmayani, and Budyanita Asrun. "Klasifikasi Sosial Ekonomi Menggunakan Naïve Bayes Classifier." Dewantara Journal of Technology 2, no. 2 (2021): 70–74. http://dx.doi.org/10.59563/djtech.v2i2.138.

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Data mining meliputi beberapa metode untuk membantu pengambilan keputusan salah satunya adalah metode klasifikasi. Metode klasifikasi meliputi beberapa cara salah satunya adalah Naive Bayes Classifier. Model Naive Bayes didasarkan pada teorema Bayes yang memiliki kemampuan klasifikasi serupa dengan decision tree. Pemanfaatan metode klasifikasi Naive Bayes Classifier digunakan pada penelitian ini dengan menggunakan data sosial ekonomi keluruhan Amessangeng Kota Palopo. Dengan mengambil sampel pada masyarakat dan menggunakan variabel-variabel predictor yang dapat digunakan menghasilkan suatu kes
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Liu, Laura, Hyungsik Roger Moon, and Frank Schorfheide. "Forecasting With Dynamic Panel Data Models." Econometrica 88, no. 1 (2020): 171–201. http://dx.doi.org/10.3982/ecta14952.

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This paper considers the problem of forecasting a collection of short time series using cross‐sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross‐sectional information to transform the unit‐specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a nonparame
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Robertson, David E., and Q. J. Wang. "A Bayesian Approach to Predictor Selection for Seasonal Streamflow Forecasting." Journal of Hydrometeorology 13, no. 1 (2012): 155–71. http://dx.doi.org/10.1175/jhm-d-10-05009.1.

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Abstract Statistical methods commonly used for forecasting climate and streamflows require the selection of appropriate predictors. Poorly designed predictor selection procedures can result in poor forecasts for independent events. This paper introduces a predictor selection method for the Bayesian joint probability modeling approach to seasonal streamflow forecasting at multiple sites. The method compares forecasting models using a pseudo-Bayes factor (PsBF). A stepwise expansion of a base model is carried out by including the candidate predictor with the highest PsBF that exceeds a selection
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Wu, Yaning, Song Huang, Haijin Ji, Changyou Zheng, and Chengzu Bai. "A novel Bayes defect predictor based on information diffusion function." Knowledge-Based Systems 144 (March 2018): 1–8. http://dx.doi.org/10.1016/j.knosys.2017.12.015.

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Hashem, Atef F., and Alaa H. Abdel-Hamid. "Statistical Prediction Based on Ordered Ranked Set Sampling Using Type-II Censored Data from the Rayleigh Distribution under Progressive-Stress Accelerated Life Tests." Journal of Mathematics 2023 (March 30, 2023): 1–19. http://dx.doi.org/10.1155/2023/5211682.

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The objective of ranked set sampling is to gather observations from a population that is more likely to cover the population’s full range of values. In this paper, the ordered ranked set sample is obtained using the idea of order statistics from independent and nonidentically distributed random variables under progressive-stress accelerated life tests. The lifetime of the item tested under normal conditions is suggested to be subject to the Rayleigh distribution with a scale parameter satisfying the inverse power law such that the applied stress is a nonlinear increasing function of time. Cons
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Alvarez, R. Michael, Delia Bailey, and Jonathan N. Katz. "An Empirical Bayes Approach to Estimating Ordinal Treatment Effects." Political Analysis 19, no. 1 (2011): 20–31. http://dx.doi.org/10.1093/pan/mpq033.

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Ordinal variables—categorical variables with a defined order to the categories, but without equal spacing between them—are frequently used in social science applications. Although a good deal of research exists on the proper modeling of ordinal response variables, there is not a clear directive as to how to model ordinal treatment variables. The usual approaches found in the literature for using ordinal treatment variables are either to use fully unconstrained, though additive, ordinal group indicators or to use a numeric predictor constrained to be continuous. Generalized additive models are
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Arumi, Endah Ratna, Sumarno Adi Subrata, and Anisa Rahmawati. "Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 2 (2023): 201–7. http://dx.doi.org/10.29207/resti.v7i2.4438.

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Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data
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Burghardt, Thomas P., and Katalin Ajtai. "Neural/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype." Journal of Molecular and Cellular Cardiology 119 (June 2018): 19–27. http://dx.doi.org/10.1016/j.yjmcc.2018.04.006.

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Tesis sobre el tema "Bayes predictor"

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Zerbeto, Ana Paula. "Melhor preditor empírico aplicado aos modelos beta mistos." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09042014-132109/.

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Os modelos beta mistos são amplamente utilizados na análise de dados que apresentam uma estrutura hierárquica e que assumem valores em um intervalo restrito conhecido. Com o objetivo de propor um método de predição dos componentes aleatórios destes, os resultados previamente obtidos na literatura para o preditor de Bayes empírico foram estendidos aos modelos de regressão beta com intercepto aleatório normalmente distribuído. O denominado melhor preditor empírico (MPE) proposto tem aplicação em duas situações diferentes: quando se deseja fazer predição sobre os efeitos individuais de novos elem
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Ayme, Alexis. "Supervised learning with missing data : a non-asymptotic point of view." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS252.

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Les valeurs manquantes sont courantes dans la plupart des ensembles de données du monde réel, en raison de la combinaison de sources multiples et d'informations intrinsèquement manquantes, telles que des défaillances de capteurs ou des questions d'enquête sans réponse. La présence de valeurs manquantes empêche souvent l'application d'algorithmes d'apprentissage standard. Cette thèse examinevaleurs manquantes dans un contexte de prédiction, visant à obtenir des prédictions précises malgré l'occurrence de données manquantes dans les données d'apprentissage et de test. L'objectif de cette thèse e
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Laws, David Joseph. "A Bayes decision theoretic approach to the optimal design of screens." Thesis, University of Newcastle Upon Tyne, 1997. http://hdl.handle.net/10443/648.

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An item may be said to reach a standard suitable for use if it has some prescribed attributes. Supposet hat a variable 2: measurest he standard and TE, qT. if an item has the desired attributes. The variable -T may be very expensive to measure and so, some cheaper to measure screening variables, X say, correlated to I may be used to classify items. The purpose of screen design is to determine CX, the region of X space, for which an item should be said to reach the standard. If the error probabilities of classifying an item based on X are very high it may be economical to measure IT. Chapter 2
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Wong, Hubert. "Small sample improvement over Bayes prediction under model uncertainty." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ56646.pdf.

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Dahlgren, Lindström Adam. "Structured Prediction using Voted Conditional Random FieldsLink Prediction in Knowledge Bases." Thesis, Umeå universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-140692.

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Knowledge bases are useful in the validation of automatically extracted information, and for hypothesis selection during the extraction process. Building knowledge bases is a dfficult task and the process is bound to miss facts. Therefore, the existence of facts can be estimated using link prediction, i.e., by solving the structured prediction problem.It has been shown that combining directly observable features with latent features increases performance. Observable features include, e.g., the presence of another chain of facts leading to the same end point. Latent features include, e.g, prope
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Liu, Benmei. "Hierarchical Bayes estimation and empirical best prediction of small-area proportions." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9149.

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Thesis (Ph.D.) -- University of Maryland, College Park, 2009.<br>Thesis research directed by: Joint Program in Survey Methodology. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Bakal, Mehmet. "Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/90.

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Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process.
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Khan, Imran Qayyum. "Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4586.

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The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods
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Wang, Kai. "Novel computational methods for accurate quantitative and qualitative protein function prediction /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/11488.

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Fredette, Marc. "Prediction of recurrent events." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1142.

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In this thesis, we will study issues related to prediction problems and put an emphasis on those arising when recurrent events are involved. First we define the basic concepts of frequentist and Bayesian statistical prediction in the first chapter. In the second chapter, we study frequentist prediction intervals and their associated predictive distributions. We will then present an approach based on asymptotically uniform pivotals that is shown to dominate the plug-in approach under certain conditions. The following three chapters consider the prediction of recurrent events. The
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Libros sobre el tema "Bayes predictor"

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S, Jacobson Nathan, Ritzert Frank J, and NASA Glenn Research Center, eds. Computational thermodynamic study to predict complex phase equilibria in the nickel-base superalloy René N6. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kenkyūjo, Bōsai Kagaku Gijutsu. Jishindō yosoku chizu no kōgaku riyō: Jishin hazādo no kyōtsū jōhō kiban o mezashite : Jishindō Yosoku Chizu Kōgaku Riyō Kentō Iinkai hōkokusho = Engineering application of the national seismic hazard program : seismic hazard information sharing bases. Bōsai Kagaku Gijutsu Kenkyūjo, 2004.

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A, Boxwell D., Spencer R. H, Ames Research Center, and United States. Army Aviation Research and Technology Activity., eds. Review and analysis of the DNW/model 360 rotor acoustic data base. National Aeronautics and Space Administration, Ames Research Center, 1989.

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Ebeling, Charles E. The determination of operational and support requirements and costs during the conceptual design of space systems: Final report : under grant no. NAG-1-1327. University of Dayton, Engineering Management and Systems Dept., 1992.

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United States. National Aeronautics and Space Administration., ed. The determination of operational and support requirements and costs during the conceptual design of space systems: Interim report. University of Dayton, Engineering Management and Systems Dept., 1991.

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D, Perrin D. pKa Prediction for Organic Acids and Bases. Springer, 2012.

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Perrin, D. PKa Prediction for Organic Acids and Bases. Springer London, Limited, 2013.

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Largescale Inference Empirical Bayes Methods For Estimation Testing And Prediction. Cambridge University Press, 2013.

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Glymour, Clark. Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology. MIT Press, 2001.

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Efron, Bradley. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press, 2013.

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Capítulos de libros sobre el tema "Bayes predictor"

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian Genomic Linear Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_6.

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AbstractThe Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.
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Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. "(Normal) Hierarchical Models with Predictors." In Bayes Rules! Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429288340-17.

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Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. "(Normal) Hierarchical Models without Predictors." In Bayes Rules! Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429288340-16.

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Johnson, Alicia A., Miles Q. Ott, and Mine Dogucu. "Posterior Inference & Prediction." In Bayes Rules! Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429288340-8.

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Bolfarine, Heleno, and Shelemyahu Zacks. "Bayes and Minimax Predictors." In Prediction Theory for Finite Populations. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2904-9_4.

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Tandel, Swapnali, and Pragya Vaishnav. "Disease Prediction Using Bayes' Theorem." In Software Engineering Approaches to Enable Digital Transformation Technologies. Routledge, 2023. http://dx.doi.org/10.1201/9781003441601-6.

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Mukhopadhyay, Parimal. "Bayes and Empirical Bayes Prediction of a Finite Population Total." In Lecture Notes in Statistics. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4612-2088-6_3.

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Steele, Brian, John Chandler, and Swarna Reddy. "The Multinomial Naïve Bayes Prediction Function." In Algorithms for Data Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45797-0_10.

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Henry, Bill, Avshalom Caspi, Terrie Moffitt, and Phil Silva. "Temperamental and Familial Predictors of Criminal Conviction." In Biosocial Bases of Violence. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-4648-8_19.

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Berger, James O., and Luis R. Pericchi. "On The Justification of Default and Intrinsic Bayes Factors." In Modelling and Prediction Honoring Seymour Geisser. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2414-3_17.

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Actas de conferencias sobre el tema "Bayes predictor"

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Xu, Zhipeng, Yabing Yao, and Ning Ma. "Mutual information higher-order link prediction based on Naive Bayes." In 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI). IEEE, 2024. http://dx.doi.org/10.1109/iotaai62601.2024.10692526.

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Nieto-Chaupis, Huber. "The Geometrical Bayes Theorem and Probabilistic Prediction of Next Global Pandemic." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698141.

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Mohamed Elhadi Hussen, Ali Abdulsamea, and Ahmad Saikhu. "Modeling Of Student Graduation Prediction Using the Naive Bayes Classifier Algorithm." In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024. http://dx.doi.org/10.1109/iccit62134.2024.10701117.

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Antony, Ashin, Devi A, and Kuruvilla Varghese. "High Throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor." In 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021. http://dx.doi.org/10.1109/i2ct51068.2021.9418100.

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Shkurti, Lamir, and Faton Kabashi. "Albanian News Category Predictor System using a Multinomial Naïve Bayes and Logistic Regression Algorithms." In 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2021. http://dx.doi.org/10.1109/ismsit52890.2021.9604602.

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Somwanshi, Harshada, and Pramod Ganjewar. "Real-Time Dengue Prediction Using Naive Bayes Predicator in the IoT." In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2018. http://dx.doi.org/10.1109/icirca.2018.8596796.

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K, Sibhi, Thanvir Ibrahim S, Akil Malik, and Praveen Joe I. R. "Career Prediction Using Naive Bayes." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917745.

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Wang, Tao, and Wei-hua Li. "Naive Bayes Software Defect Prediction Model." In 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677057.

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Biteau, J. "Pressure, Seals and Traps: the Bases for the Petroleum System to Work Efficiently." In Second EAGE Workshop on Pore Pressure Prediction. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201900497.

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Meiriza, Allsela, Endang Lestari, Pacu Putra, Ayu Monaputri, and Dini Ayu Lestari. "Prediction Graduate Student Use Naive Bayes Classifier." In Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/aisr.k.200424.056.

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Informes sobre el tema "Bayes predictor"

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Knox, Thomas, James Stock, and Mark Watson. Empirical Bayes Forecasts of One Time Series Using Many Predictors. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/t0269.

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Cheng, Hao, Rohan L. Fernando, and Dorian J. Garrick. Three Different Gibbs Samplers for BayesB Genomic Prediction. Iowa State University, 2014. http://dx.doi.org/10.31274/ans_air-180814-1152.

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Ceylan, Ismail Ilkan, Stefan Borgwardt, and Thomas Lukasiewicz. Most Probable Explanations for Probabilistic Database Queries. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2023.220.

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Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum
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Girolamo Neto, Cesare, Rodolfo Jaffe, Rosane Cavalcante, and Samia Nunes. Comparacao de modelos para predicao do desmatamento na Amazonia brasileira. ITV, 2021. http://dx.doi.org/10.29223/prod.tec.itv.ds.2021.25.girolamoneto.

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O presente relatório contém resultados parciais do projeto “Definição de áreas prioritárias para recuperação florestal”, referentes a atividade “Uso e comparação da acurácia de diferentes modelos preditivos de desmatamento na Amazônia”. O objetivo deste estudo foi a implementação de modelos preditivos de desmatamento na Amazônia brasileira com base nas técnicas de Random Forest (RF), Spatial Random Forest (SpRF) e Integrated Nested Laplace Approximations (INLA) e comparação dos erros obtidos com cada modelo. Uma base de dados geográficos foi gerada por meio da integração de dados de diversas i
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Soloviev, V., and V. Solovieva. Quantum econophysics of cryptocurrencies crises. [б. в.], 2018. http://dx.doi.org/10.31812/0564/2464.

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From positions, attained by modern theoretical physics in understanding of the universe bases, the methodological and philosophical analysis of fundamental physical concepts and their formal and informal connections with the real economic measuring is carried out. Procedures for heterogeneous economic time determination, normalized economic coordinates and economic mass are offered, based on the analysis of time series, the concept of economic Plank's constant has been proposed. The theory has been approved on the real economic dynamic's time series, related to the cryptocurrencies market, the
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The current bases for roof fall prediction at WIPP and a preliminary prediction for SPDV Room 2. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10191310.

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ROTATIONAL STIFFNESS MODEL FOR SHALLOW EMBEDDED STEEL COLUMN BASES. The Hong Kong Institute of Steel Construction, 2022. http://dx.doi.org/10.18057/icass2020.p.308.

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Resumen
Embedded steel column bases are normally idealized as fixed boundary condition in the analysis of steel frameworks. In practice, the embedded column bases sometimes have embedded depth notably smaller than that recommended by the structural design codes. In such a case, the fixed boundary assumption may overestimate the stiffness of the bases to a large extent. In light of this, this paper proposes a theoretical model for the accurate prediction of the rotational stiffness of the shallow embedded steel column bases, with the combined effects of key influencing factors, including the axial load
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