Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 29 for “"bayesian model averaging"”.
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Multivariate Applications of Bayesian Model Averaging
… standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number of …
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Bayesian model averaging for mathematics achievement growth rate trends
In this study, we investigated the use of Bayesian model averaging (BMA) for latent growth curve models. We used the Trends in International Mathematics and Science Study (TIMSS) to predict growth rates in 8th-grade students' mathematics achievement. The dataset on male and female students' …
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Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models
Bayesian Model Averaging (BMA) is a new area in modern applied statistics that provides data analysts with an efficient tool for discovering promising models and obtaining esti-mates of their posterior probabilities via Markov chain Monte Carlo (MCMC). These probabilities can be further used as …
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Bayesian model averaging on hydraulic conductivity estimation and groundwater head prediction
… of aquifer properties and the uncertainties of model parameters. This study introduces a Bayesian model averaging (BMA) method along with multiple generalized parameterization (GP) methods to identify hydraulic conductivity and along with multiple simulation models to predict groundwater head …
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Topics in Bayesian sample size determination and Bayesian model selection.
This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the …
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Bayesian Saltwater Intrusion Prediction and Remediation Design under Uncertainty
… resources. However, the groundwater simulation models are subjected to uncertainty in their predictions. The goals of this research are to: (1) quantify the uncertainty in the groundwater model predictions and (2) investigate the impact of the quantified uncertainty on the aquifer remediation …
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Μοντέλα σύλληψης-επανασύλληψης για την εκτίμηση του μεγέθους δύσκολα προσεγγίσιμων πληθυσμών
… (M0, Mt) όσο και με Μπεϋζιανή προσέγγιση: Bayesian Model Averaging (BMA) με μη πληροφοριακό εκ των προτέρων κατανομή και με λογαριθμοκανονική (log-normal) prior. Για κάθε μοντέλο υπολογίστηκαν σημειακές εκτιμήσεις και 95% διαστήματα εμπιστοσύνης ή credible intervals. Αποτελέσματα: Για τους …
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Three Essays in Empirical Economics
… of RTA trade effects. The third essay uses Bayesian Model Averaging (BMA) to study the effect of membership in the General Agreement on Tariffs and Trade (GATT), the predecessor to the World Trade Organization (WTO), and the WTO on trade flows. Existing GATT/WTO literature is not univocal as …
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Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data
… allow. We then further extend this method into model selection. In the special case where the unobserved covariates are assumed normally distributed we use the Bayesian Model Averaging method to average the models, select the highest probability model and do variable assessment. Accuracy in …
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Bayesian and Frequentist Approaches for the Analysis of Multiple Endpoints Data Resulting from Exposure to Multiple Health Stressors.
… chemicals. It consists of fitting a mathematical model to the exposure data and the BMD is the dose expected to result in a pre-specified response or benchmark response (BMR). Most available exposure data are from single chemical exposure, but living objects are exposed to multiple sources of …
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A short-term ensemble wind-speed forecasting system for wind power applications
… Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed …
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Removal of Per- and Poly-fluoroalkyl Substances (PFAS) from Water: A Computational Approach
… kinetically favorable pathway. Complementary ML models developed for PFAS detection using o-phenylenediamine molecularly imprinted polymer electrochemical sensors demonstrate high classification accuracy, particularly with support vector machines enhanced through Bayesian model averaging. …
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Enhancing Risk Stratification for Substance Use Disorder, Depression, and Anxiety through Quantitative Predictive Analytics
… of these conditions, the study developed models to identify individuals likely to develop these mental health disorders. It also assessed the accuracy and reliability of various machine learning models over a year. Results demonstrated that Random Forest, Neural Networks, and XGBoost …
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Essays in applied econometrics
… Curve for Brazil?: An Investigation Using Bayesian Model Averaging and Nonparametric Model Selection. Brazil has become one of the major emerging countries in the world, registering a promising development scenario. However, the income inequality in Brazil remains higher if compared to …
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Quantifying sources of uncertainty in regional climate model scenarios for Ireland
This thesis develops a novel framework for model skill assessment and the generation of probabilistic future climate scenarios. Traditional approaches to model validation assume that skill in simulating the mean climate is a valid indicator of skill in modelling the climate system. However, without …
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Bayesian autoencoders for anomaly detection: Design, uncertainty quantification, and explainability with industrial applications
… systems have facilitated the use of powerful models such as autoencoders (AEs), a class of neural networks (NNs), to achieve state-of-the-art results in anomaly detection. Nevertheless, there are growing concerns regarding the safety and trustworthiness of AEs, as recent studies have reported …
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Model Uncertainty & Model Averaging Techniques
… research is to shed more light on the issue of model uncertainty in applied econometrics in general and cross-country growth as well as happiness and well-being regressions in particular. Model uncertainty consists of three main types: theory uncertainty, focusing on which principal determinants …
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Why are microcredit interest rates in sub-Saharan Africa so persistently high? Testing the predictions of theoretical models
… empirical approaches are used in the study: Bayesian Model Averaging, fixed effects, Generalised Method of Moments, and Ordinary Least Squares. The results reveal evidence of the following: (i) the operating costs associated with providing small loans, unexploited economies of scale, and …
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Essays on Agricultural and Regional Development
… 1949-2016 and attempts to address the ad hoc model selection problem common in previous studies. Among the econometric modeling strategies in previous literature, Bayesian Model Averaging (BMA) and Bayesian Hierarchical Model (BHM) are two promising methods to solve the issue of model …
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Advanced framework for assessment and reduction of model form uncertainty of the closure laws in thermal-hydraulics codes
Accurate modeling of the two-phase flow phenomena is important for the safety analysis of light water reactors. The modeling approach must balance model resolution with computational feasibility. The direct implementation of local instant formulation is not practical for most engineering …
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