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Literatura académica sobre el tema "Bayes predictor"

Autor: Grafiati

Publicado: 30 de noviembre de 2024

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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 empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single base learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance.

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2

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 adopted as a potential solver to predict three target reservoir characters. To guarantee the predicting quality of LightGBM, continuous restricted Boltzmann machine (CRBM) and Bayesian optimization (Bayes) are introduced as assistants to enhance the significance of input logs and the setting of employed hyperparameters. Thereby, a new hybrid predictor, named CRBM-Bayes-LightGBM, is proposed for the prediction task. To validate the working performance of the proposed predictor, the basic data derived from the member of Chang 8, Jiyuan Oilfield, Ordos Basin, Northern China, is collected to launch the corresponding experiments. Additionally, to highlight the validating effect, three sophisticated predictors, including k-nearest neighbors (KNN), support vector regression (SVR), and random forest (RF), are introduced as competitors to implement a contrast. Since ensemble learning models universally will cause an underfitting issue when dealing with a small-volumetric dataset, transfer learning in this circumstance will be employed as an aided technique for the core predictor to achieve a satisfactory prediction. Then, three experiments are purposefully designed for four validated predictors, and given a comprehensive analysis of the gained experimented results, two critical points are concluded: (1) compared to three competitors, LightGBM-cored predictor has capability to produce more reliable predicted results, and the reliability can be further improved under a usage of more learning samples; (2) transfer learning is really functional in completing a satisfactory prediction for a small-volumetric dataset and furthermore has access to perform better when serving for the proposed predictor. Consequently, CRBM-Bayes-LightGBM combined with transfer learning is solidly demonstrated by a stronger capability and an expected robustness on the prediction of porosity, permeability, and water saturation, which then clarify that the proposed predictor can be viewed as a preferential selection when geologists, geophysicists, or petrophysicists need to finalize a characterization of sandy-mud reservoirs.

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3

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 kesimpulan demi menghasilkan informasi yang akurat yang dapat membantu pengambilan keputusan terhadap kebijakan sosial ekonomi di Kelurahan Amessangeng

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4

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 nonparametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated random effects distribution as known (ratio optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application, we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.

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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 threshold. Predictors representing the initial catchment conditions are selected on their ability to forecast streamflows and predictors representing future climate influences are selected on their ability to forecast rainfall. The final forecasting model combines selected predictors representing both initial catchment conditions and future climate influences to jointly forecast seasonal streamflows and rainfall. Applications of the predictor selection method to two catchments in eastern Australia show that the best predictors representing initial catchment conditions and future climate influences vary with location and forecast date. Antecedent streamflows are the best indicator of the initial catchment conditions. Predictors representing future climate influences are only selected for forecasts made between July and January. Indicators of El Niño dominate the selected predictors representing future climate influences. The skill of streamflow forecasts varies considerably between locations and throughout the year. Skill scores for the perennial streams of the Goulburn River catchment exceed 40% for several seasons, while for the intermittent streams in the Burdekin River catchment, the skill scores are lower.

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6

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. Considering the type-II censoring scheme, one-sample prediction for censored lifetimes is discussed. Numerous point predictors including the Bayes point predictor, conditional median predictor, and best unbiased predictor for future order statistics are discussed. Additionally, conditional prediction intervals for future order statistics are also studied. The theoretical findings reported in this work are shown by illustrative examples based on simulated data as well as real data sets. The effectiveness of the prediction methods is then evaluated by a Monte Carlo simulation study.

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8

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 a useful exception to these assumptions. In contrast to the generalized additive modeling approach, we propose the use of a Bayesian shrinkage estimator to model ordinal treatment variables. The estimator we discuss in this paper allows the model to contain both individual group—level indicators and a continuous predictor. In contrast to traditionally used shrinkage models that pull the data toward a common mean, we use a linear model as the basis. Thus, each individual effect can be arbitrary, but the model “shrinks” the estimates toward a linear ordinal framework according to the data. We demonstrate the estimator on two political science examples: the impact of voter identification requirements on turnout and the impact of the frequency of religious service attendance on the liberality of abortion attitudes.

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9

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 sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.

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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 elementos de grupos que já fizeram parte da base de ajuste e quando os grupos não pertenceram à tal base. Estudos de simulação foram delineados e seus resultados indicaram que o desempenho do MPE foi eficiente e satisfatório em diversos cenários. Ao utilizar-se da proposta na análise de dois bancos de dados da área da saúde, observou-se os mesmos resultados obtidos nas simulações nos dois casos abordados. Tanto nas simulações, quanto nas análises de dados reais, foram observados bons desempenhos. Assim, a metodologia proposta se mostrou promissora para o uso em modelos beta mistos, nos quais se deseja fazer predições.<br>The mixed beta regression models are extensively used to analyse data with hierarquical structure and that take values in a restricted and known interval. In order to propose a prediction method for their random components, the results previously obtained in the literature for the empirical Bayes predictor were extended to beta regression models with random intercept normally distributed. The proposed predictor, called empirical best predictor (EBP), can be applied in two situations: when the interest is predict individuals effects for new elements of groups that were already analysed by the fitted model and, also, for elements of new groups. Simulation studies were designed and their results indicated that the performance of EBP was efficient and satisfatory in most of scenarios. Using the propose to analyse two health databases, the same results of simulations were observed in both two cases of application, and good performances were observed. So, the proposed method is promissing for the use in predictions for mixed beta regression models.

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2

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 est d'analyser théoriquement des algorithmes spécifiques pour obtenir des garanties d'échantillons finis. Nous dérivons des bornes inférieures minimax sur le risque des prédictions linéaires en présence de valeurs manquantes. Ces bornes inférieures dépendent de la distribution du motif de valeurs manquantes et peuvent croître de manière exponentielle avec la dimension. Nous proposons une méthode très simple consistant à appliquer la procédure des moindres carrés uniquement aux motifs de valeurs manquantes les plus fréquents. Une telle méthode simple se révèle être une procédure presque minimax-optimale, qui s'écarte de l'algorithme des moindres carrés appliqué à tous les motifs de valeurs manquantes. Par la suite, nous explorons la méthode de l'imputation puis régression, où l'imputation est effectuée en utilisant l'imputation naïve par zéro, et l'étape de régression est réalisée via des modèles linéaires, dont les paramètres sont appris via la descente de gradient stochastique. Nous démontrons que cette méthode très simple offre de fortes garanties pour des échantillons finis dans des contextes de grande dimension. Plus précisément, nous montrons que le biais de cette méthode est inférieur au biais de la régression ridge. Étant donné que la régression ridge est souvent utilisée en haute dimension, cela prouve que le biais des données manquantes (via l'imputation par zéro) est négligeable dans certains contextes de grande dimension. Enfin, nous étudions différents algorithmes pour gérer la classification linéaire en présence de données manquantes (régression logistique, perceptron, LDA). Nous prouvons que la LDA est le seul modèle qui peut être valide pour des données complètes et manquantes dans certains contextes génériques<br>Missing values are common in most real-world data sets due to the combination of multiple sources andinherently missing information, such as sensor failures or unanswered survey questions. The presenceof missing values often prevents the application of standard learning algorithms. This thesis examinesmissing values in a prediction context, aiming to achieve accurate predictions despite the occurrence ofmissing data in both training and test datasets. The focus of this thesis is to theoretically analyze specific algorithms to obtain finite-sample guarantees. We derive minimax lower bounds on the excess risk of linear predictions in presence of missing values.Such lower bounds depend on the distribution of the missing pattern, and can grow exponentially withthe dimension. We propose a very simple method consisting in applying Least-Square procedure onthe most frequent missing patterns only. Such a simple method turns out to be near minimax-optimalprocedure, which departs from the Least-Square algorithm applied to all missing patterns. Followingthis, we explore the impute-then-regress method, where imputation is performed using the naive zeroimputation, and the regression step is carried out via linear models, whose parameters are learned viastochastic gradient descent. We demonstrate that this very simple method offers strong finite-sampleguarantees in high-dimensional settings. Specifically, we show that the bias of this method is lowerthan the bias of ridge regression. As ridge regression is often used in high dimensions, this proves thatthe bias of missing data (via zero imputation) is negligible in some high-dimensional settings. Thesefindings are illustrated using random features models, which help us to precisely understand the role ofdimensionality. Finally, we study different algorithm to handle linear classification in presence of missingdata (logistic regression, perceptron, LDA). We prove that LDA is the only model that can be valid forboth complete and missing data for some generic settings

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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 deals with this idea in the context of a very simple two-stage set-up in which, at the first stage of the screen a univariate screening variable X is measured. Some items are sentenced as acceptable or unacceptable, and the remainder are passed on to the second stage at which T is determined. The optimal screen is found that minimises cost, where costs are given for misclassifying items and for measuring the variables. The variable T is assumed binary and the model for TIX is a probit regression model. In designing a two-stage screen, Chapter 3 considers: (a) a general stochastic structure for (1, X), (b) a general loss function set up for misclassification costs and (c) assumes no fixed form for the screen. Also in Chapter 3, we consider a scenario in which a statistical goal or constraint is imposed in addition to the decision-theoretic target of minimising expected cost. In Chapter 4 we consider a sequential screen that operates as follows. At each stage of a sequence a covariate is measured and items may be accepted as suitable, discarded or passed on to the next stage. At the final stage the performance variable T is measured. Returning to the simple one-stage screen based solely on measuring covariates, Chapter 5 poses the question of how many and which covariates to include as part of the screen.

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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, properties that are not modelled by facts on the form subject-predicate-object, such as being a good actor. Observable graph features are modelled using the Path Ranking Algorithm, and latent features using the bilinear RESCAL model. Voted Conditional Random Fields can be used to combine feature families while taking into account their complexity to minimize the risk of training a poor predictor. We propose a combined model fusing these theories together with a complexity analysis of the feature families used. In addition, two simple feature families are constructed to model neighborhood properties.The model we propose captures useful features for link prediction, but needs further evaluation to guarantee effcient learning. Finally, suggestions for experiments and other feature families are given.

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6

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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7

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. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used.

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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 which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

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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 third chapter presents different prediction models when these events can be modeled using homogeneous Poisson processes. Amongst these models, those using random effects are shown to possess interesting features. In the fourth chapter, the time homogeneity assumption is relaxed and we present prediction models for non-homogeneous Poisson processes. The behavior of these models is then studied for prediction problems with a finite horizon. In the fifth chapter, we apply the concepts discussed previously to a warranty dataset coming from the automobile industry. The number of processes in this dataset being very large, we focus on methods providing computationally rapid prediction intervals. Finally, we discuss the possibilities of future research in the last chapter.

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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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2

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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6

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 a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

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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 instituições brasileiras, como IBGE, MMA e INPE, utilizando células de 25 x 25 km e uma janela temporal de um ano. Os principais drivers de desmatamento identificados estão relacionados à fragmentação florestal e à expansão de áreas de pastagem na Amazônia, corroborando com outros trabalhos encontrados em literatura. A modelagem obteve melhores resultados com o uso dos modelos RF e SpRF em relação aos modelos do tipo INLA, com menores valores de erro médio quadrático obtido em conjuntos de dados de treinamento e validação dos algoritmos. A previsão de desmatamento para o ano de 2020 foi de 31 mil km2 , dados que apresentam uma superestimava devido ao método utilizado para o cálculo do desmatamento. Entre as ações identificadas que podem ser adotadas em trabalhos futuros para melhorar a previsão do desmatamento, cita-se o uso da abordagem CLUE e a melhoria de algumas bases de dados utilizada, a exemplo da malha viária.

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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 achieved results are open for discussion. Then, combined the empirical cross-correlation matrix with the random matrix theory, we mainly examine the statistical properties of cross-correlation coefficient, the evolution of average correlation coefficient, the distribution of eigenvalues and corresponding eigenvectors of the global cryptocurrency market using the daily returns of 15 cryptocurrencies price time series across the world from 2016 to 2018. The result indicated that the largest eigenvalue reflects a collective effect of the whole market, practically coincides with the dynamics of the mean value of the correlation coefficient and very sensitive to the crisis phenomena. It is shown that both the introduced economic mass and the largest eigenvalue of the matrix of correlations can serve as quantum indicator-predictors of crises in the market of cryptocurrencies.

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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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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 in the column, the shear deformation of the embedded column, the rotational constraint provided by the column end plate, and the stiffness contribution from the anchor bolts. To achieve this, the calculation of the rotational stiffness of the base plate is refined in this paper based on previous study, which can consider the contribution from the anchor bolts, and incorporated in the Winkler foundation beam model, giving the expression of the initial rotational stiffness of the embedded column base. The proposed model is validated by the experimental and finite element simulation results.

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