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Showing 1 to 20 of 209 for “"bayesian model"”.
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Predictive Alternatives in Bayesian Model Selection
Model comparison and hypothesis testing is an integral part of all data analyses. In this thesis, I present two new families of information criteria that can be used to perform model comparison. In Chapter 1, I review the necessary background to motivate the thesis. Of particular interest is the …
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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 determination for categorical data survey
… this thesis is to present an investigation of a Bayesian approach to the analysis of categorical survey data, arising from designs including simple random sampling, finite population sampling, stratification, and cluster sampling. We focus on Bayesian methods for model selection and model …
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Revision of Subjective Probabilities Under a Bayesian Model
… subjective probability judgments conform to the Bayesian model of mathematical probability theory; more specifically, the degree to which subjective probability estimates for intersections of events approximate the product of the subjective judgments for component events.
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Bayesian model-based clustering of multi-source data
… of data generated across multiple sources. Bayesian mixture models and their extensions are effective tools for partition inference in this setting as we can use these to describe and infer the relationship between different sources. I consider applying such methods to two cases of …
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Bayesian model selection with applications to radio astronomy
… of two main parts, both of which focus on Bayesian methods and the problem of model selection in particular. The first part investigates a new approach to computing the Bayes factor for model selection without needing to compute the Bayesian evidence, while the second part shows, through an …
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Three essays in macroeconomic forecasting using Bayesian model selection
This thesis explores several aspects of Bayesian model selection in time series forecasting of macroeconomic variables. The contribution is provided in three essays. In the first essay (Chapter 2) I forecast quarterly US inflation based on the generalized Phillips curve using econometric methods …
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Default Bayesian model determination for generalised liner mixed models
In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation.<br/><br/>Default prior distributions for the model parameters, that are based on a unit …
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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 Selection in terms of Kullback-Leibler discrepancy
… article we investigate and develop the practical model assessment and selection methods for Bayesian models, when we anticipate that a promising approach should be objective enough to accept, easy enough to understand, general enough to apply, simple enough to compute and coherent enough to …
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Detecting episodes of star formation using Bayesian model selection.
Bayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy Distribution (SED) of galaxies. This method is validated by simulating galaxy …
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Using a Bayesian model for bankruptcy prediction : a comparative approach
… cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are …
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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 Model Selection for Spatial Data and Cost-constrained Applications
Bayesian model selection is a useful tool for identifying an appropriate model class, dependence structure, and valuable predictors for a wide variety of applications. In this work we consider objective Bayesian model selection where no subjective information is available to inform priors on model …
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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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A pseudo-Bayesian model-based approach for noninvasive intracranial pressure estimation
… estimation method is proposed that incorporates model-based estimation within a probabilistic framework. A first-order subject-specific model of the cerebral vasculature relates arterial blood pressure with cerebral blood flow velocity. The model is solved for a range of physiologically plausible …
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Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior distributions in an objective manner, providing conditions under which …
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A representative agent asset pricing model with Bayesian model averagin of copula-based densities
Esta tesis solo está en formato papel por lo que se debe consultar en la propia Biblioteca Di Tella. La consulta se hace solo bajo reserva escribiendo a serviciosbiblio@utdt.edu.
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