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Bayesian thesis

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Bayesian regression analysis. Sara Evans , University of Louisville. Regression analysis is a statistical method used to relate a variable of interest, typically y the dependent variable , to a set of independent variables, usually, X1, X2, The goal is to build a model that assists statisticians in describing, controlling, and predicting the dependent variable based on the independent variable s.
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Thompson sampling - Wikipedia

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Philosophy of artificial intelligence

In some cases, maps of soil characteristics and other agronomically relevant properties are created with small data sets and accuracy can be a challenge. PhD candidate Luc Steinbuch dug into this challenge by using Bayesian geostatistics to see how accuracy might be improved in cases with limited point data. For this research, Luc explored the limitations of soil mapping in the case of small data sets, how to include legacy soil data the Bayesian way, and how to correctly assess spatial prediction uncertainty for crop yields and soil properties, on point level but also for a province or country as a whole. His thesis uses data from various case studies, such as sorghum and millet yield in West Africa and soil ripening in land reclaimed from former lakes in the Netherlands.
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The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents a unified variational Bayesian VB framework which approximates these computations in models with latent variables using a lower bound on the marginal likelihood. Chapter 1 presents background material on Bayesian inference, graphical models, and propagation algorithms.
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