Browsing by Subject "Bayesian inference"
Now showing items 1-10 of 10
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(2023-01-06)We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. We model each score component as a smooth term, an extension of ...
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(2022-06-23)We consider the estimation of the marginal likelihood in Bayesian statistics, a essential and important task known to be computationally expensive when the dimension of the parameter space is large. We propose a general ...
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(2017-08-16)Several empirical or analytical/semi-analytical simulation models have been developed to assess the Estimated Ultimate Recovery (EUR) of an oil or gas formation for a short or long term of production. Furthermore, providing ...
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(2021-01-06)We propose a suite of Bayesian learning methods to address challenges arising from task and data heterogeneity in life science applications. First, we develop a novel multi-domain negative binomial (NB) factorization ...
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(Texas A&M University, 2007-09-17)The increased collection of high-dimensional data in various fields has raised a strong interest in clustering algorithms and variable selection procedures. In this disserta- tion, I propose a model-based method that ...
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(2013-12-06)The aim of the current work is to develop a Bayesian approach to model and simulate the behavior of materials with nonlinear mechanical response in the presence of significant uncertainties in the experimental data as well ...
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(2022-08-18)Artificial Intelligent and Machine Learning (AI/ML) systems have been widely adopted with the increasing availability of data in a variety of applications such as computer vision, activity recognition, autonomous driving, ...
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(2023-03-02)This article focuses on a multi-modal imaging data application where structural/anatomical information from grey matter (GM) and brain connectivity information in the form of a brain connectome network from functional ...
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(2019-10-17)Offshore site characterization for geotechnical construction projects relies on geological and geophysical survey data because geotechnical sampling techniques have limitations for offshore environments. However, the seismic ...
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(2023-05-16)Bayesian computation of high dimensional linear regression models with popular Gaussian scale mixture prior distributions using Markov Chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive ...