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 479 for “"Bayes"”.
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Clustered Naive Bayes
… In this thesis, I present the Clustered Naive Bayes classifier, a hierarchical extension of the classic Naive Bayes classifier that ties several distinct Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters. A priori, the model assumes that there exists a …
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Some parametric empirical Bayes techniques
… considers two distinct aspects of the empirical Bayes decision problem. The first aspect considered is the problem or point estimation and hypothesis testing. The second aspect considered is that of estimating the prior distribution and then the estimation of posterior distribution and confidence …
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Masquerade detection using fortified naive Bayes
… Roy Maxion and Kevin Killourhy utilized a Naive Bayes classifier for detection using enriched Unix command lines, which are command line entries that still contain flags and other data. They discovered a problem with users that they dubbed supermasqueraders. These were users that would avoid …
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Bayes-suggested solutions in binomial estimation
… transformation of the binomial probability. A Bayesian viewpoint is adopted temporarily to "suggest" a wide class of admissible estimators for each problem. Designated the class C of SBP estimators, it is the class of Bayes estimators derived from Symmetric Beta Priors (the class of conjugate …
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Generalized Empirical Bayes: Theory, Methodology, and Applications
The two key issues of modern Bayesian statistics are: (i) establishing a principled approach for \textit{distilling} a statistical prior distribution that is \textit{consistent} with the given data from an initial believable scientific prior; and (ii) development of a \textit{consolidated} …
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Empirical Bayes methods in time series analysis
… from experiment to experiment, the Empirical Bayes method often leads to estimators which have smaller mean squared errors than the classical estimators. Suppose there is an unobservable random variable θ, where θ ~ G(θ), usually called a prior distribution. The Bayes estimator of θ cannot be …
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Empirical Bayes procedures in time series analysis
Empirical Bayes analysis concerns the analysis of data which occur in similar recurring situations. The parameters involved in the recurring situations are generated independently from an unknown probability distribution G(θ). In many situations it is possible to use the estimates of all of the …
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Bayes Multiple Decision Functions: Theory, Computation and Application
… for each row the data may be low dimensional. A Bayesian decision-theoretic approach for this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for the use of the false …
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Webpage rank using Bayes' rule and connected components
… a connected component (CC) or simply a block. A Bayes' theorem based block-wise local PageRank computation and weighting scheme are then used to compute the final ranking vector of CCRank, which approximates the one of PageRank algorithm. The computation can be accelerated through distribute the …
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Exploration and exploitation in Bayes sequential decision problems
Bayes sequential decision problems are an extensive problem class with wide application. They involve taking actions in sequence in a system which has characteristics which are unknown or only partially known. These characteristics can be learnt over time as a result of our actions. Therefore we …
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Kausales Denken, Bayes-Netze und die Markov-Bedingung
… Kausalwissen spielt die Theorie der kausalen Bayes-Netze, die Ursache-Wirkungs-Beziehungen in gerichteten Graphen formalisiert. Zentrale Annahme dieses Ansatzes ist die Markov-Bedingung, nach der eine Variable konditionalisiert auf ihre direkten Ursachen unabhängig von allen anderen, nicht …
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Transformers as Empirical Bayes Estimators The Poisson Model
… Learning (ICL) in the setting of Empirical Bayes for the Poison Model. On the theoretical side, we demonstrate the expressibility of transformers by formulating a way to approximate the Robbins estimator, the first empirical Bayes estimator for the Poisson model. On the empirical side, we …
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Improving multi-class text classification with Naive Bayes
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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Empirical Bayes procedures in time series regression models
In this dissertation empirical Bayes estimators for the coefficients in time series regression models are presented. Due to the uncontrollability of time series observations, explanatory variables in each stage do not remain unchanged. A generalization of the results of O'Bryan and Susarla is …
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Ontology-based annotation using naive Bayes and decision trees
… and model the annotations with a group of naive Bayes classifiers, then explore the inherent relationship among different components defined by the ontology using a probabilistic decision tree model. Our solution outperforms conventional text mining approaches by taking advantage of an ontology. …
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Bayes’ Theorem: A Model for Human Probability Estimate Revision
The purpose of this study was to examine Bayes' Theorem as a model for the description of how humans utilize information based on uncertain (probabilistic) relationships between the relevant cues and the outcome-classes.
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Exact Solution of Bayes and Minimax Change-Detection Problems
The challenge of detecting a change in the distribution of data is a sequential decision problem that is relevant to many engineering solutions, including quality control and machine and process monitoring. This dissertation develops techniques for exact solution of change-detection problems with …
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Exact Solution of Bayes and Minimax Change-Detection Problems
… Change-detection problems are classified as Bayes or minimax based on the availability of information on the change-time distribution. A Bayes optimal solution uses prior information about the distribution of the change time to minimize the expected cost, whereas a minimax optimal solution …
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Contributions to the Analysis of Experiments Using Empirical Bayes Techniques
… statistical model is a difficult step in the Bayesian approach to the design and analysis of experiments. Here we address this difficulty by proposing the use of functional priors and then by working out important details for three and higher level experiments. One of the challenges presented …
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Advances in Variational Bayes Theory: Adaptation, Uncertainty Quantification, and Amortization
… have been widely used in statistical physics, Bayesian posterior approximation, and modern generative modeling. At a high level, a variational method transforms an often difficult or intractable inference problem into an optimization problem by projecting a target distribution, under an …
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