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 177 for “"Expectation-Maximization"”.
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Planar detection using modified expectation maximization
… data problem motivates the employment of the Expectation Maximization (EM) algorithm. Derivation of the EM algorithm equations proves that a closed form solution to the maximization step is impractical which leads to the proposal of a Modified Expectation Maximization (MEM) algorithm. The MEM …
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Multiresolution laser radar range profiling with the expectation-maximization algorithm
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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The Application of the Expectation-Maximization Algorithm to the Identification of Biological Models
… the data and utilize a modification of the Expectation-Maximization algorithm for training it. With our model, we explore some commonly accepted assumptions concerning sampling, discretization, and state transformations. Also, we illuminate the model complexities and interpretation …
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A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo
… Learning that is usually solved using Likelihood Maximization methods, of which the Expectation-Maximization algorithm (EM) is the most common. In this work we present an algorithm merging Markov Chain Monte Carlo methods with the EM algorithm to find qualitatively better solutions for the …
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Bayesian Expectation-Maximization-Maximization: a latent-mixture-modeling-based Bayesian algorithm for the three-parameter logistic model
The current study proposes a Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM), which is an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM). The Bayesian EMM takes full advantage of both the EMM and the Bayesian approach. The BEMM not only …
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Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm
The Expectation-Maximization (EM) algorithm is an iterative algorithm for finding the maximum likelihood estimates in problems involving missing data or latent variables. The EM algorithm can be applied to problems consisting of evidently incomplete data or missingness situations, such as truncated …
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Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation-maximization under non-Bayesian and Bayesian framework
… A mathematical framework is developed where Expectation-Maximization (EM) algorithm is implemented to quantify input model parameter uncertainty using the Maximum Likelihood Estimate (MLE) and Maximum a Posteriori (MAP) estimate. The difference between experimental measurements and nominal …
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Evaluating least absolute deviation regression as an inverse model in groundwater flow calibration
… the Least sum Absolute Deviation regression and Expectation Maximization procedures. These new FORTRAN procedures were added to the parameter estimation source code of the MODFLOW groundwater computer model. The resulting inverse MODFLOW model can now calculate the hydraulic conductivity for …
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From expectation-3-maximization to bayesian expectation-3-maximization: A latent mixture modeling-based bayesian algorithm for the 4-parameter logistic model
… a latent mixture modeling view and developed the Expectation-Maximization-Maximization-Maximization (EMMM) method. Combining the EMMM with the Bayesian approach, allowed the Bayesian Expectation-Maximization-Maximization-Maximization (BEMMM) algorithm to be proposed. First, the author compared the …
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Distributed Target Tracking and Synchronization in Wireless Sensor Networks
… a combination of hierarchical clustering and the expectation-maximization algorithm. Using numerical examples, we show that the proposed distributed particle filtering algorithm improves the accuracy and communication efficiency of distributed target tracking, and that the proposed adaptive …
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Algorithms for structural learning with decompositions
… unsupervised learning, we propose a family of Expectation Maximization [Dempster et al., 1977] algorithms called Unified Expectation Maximization (UEM) [Samdani et al., 2012a] that covers several seemingly divergent versions of EM e.g. hard EM. To efficiently add domain-specific declarative …
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Image reconstruction and imaging configuration optimization with a novel nanotechnology enabled breast tomosynthesis multi-beam X-ray system
… reconstruction (MITS), maximum likelihood expectation maximization (MLEM), ordered-subset maximum likelihood expectation maximization (OS-MLEM), simultaneous algebraic reconstruction technique (SART), were implemented to fit our system design. An accelerated MLEM algorithm was proposed, …
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Interactive Imaging via Hand Gesture Recognition.
… object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample of each different gesture, the comparability between testing image and samples of each type of gestures will …
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Using the EM Algorithm to Estimate the Difference in Dependent Proportions in a 2 x 2 Table with Missing Data.
… table when there are missing data. The Expectation-Maximization (EM) algorithm is used to obtain an estimate for the difference between correlated proportions. To obtain the standard error of this difference I employ a resampling technique known as bootstrapping. The performance of the …
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Methods for identifying regulatory grammars
… than was previously possible. We present an expectation-maximization learning algorithm that identifies enriched spatial relationships between motifs in sets of DNA sequences. For example, the method will identify spatially constrained motifs colocated in the same regulatory region. We apply …
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Parameter Estimation Techniques for Nonlinear Dynamic Models with Limited Data, Process Disturbances and Modeling Errors
… parameters in SDE models. First, an Approximate Expectation Maximization (AEM) algorithm is developed for estimating model parameters and process disturbance intensities when measurement noise variance is known. Then, a Fully-Laplace Approximation Expectation Maximization (FLAEM) algorithm is …
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Knowledge Distillation for Interpretable Clinical Time Series Outcome Prediction
… evaluate an alternative approach that uses the expectation-maximization algorithm. We analyze the interpretability of the learned states. Our results show that, although there is room for improvement in maintaining the generative performance of the model after adding the similarity constraint, …
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Application of the EM Algorithm for Mixture Models
… that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as …
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Robust Bayesian state estimation and mapping
… a batch robust SLAM algorithm that uses the Expectation- Maximization algorithm to infer both the navigation solution and the measurement information matrices. Inferring the information matrices allows the algorithm to reduce the impact of outliers on the SLAM solution while the …
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