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.
Results
Showing 1 to 16 of 16 for “"Expectation maximisation"”.
-
Bayesian approaches to time-frequency inverse problems
… prior, Chapter 4 develops a high-performance expectation–maximisation algorithm for sparse reconstruction of corrupted audio signals. Chapters 5 and 6 mark a departure from the sparse approximation paradigm, investigating instead the potential of low-rank latent structures in time-frequency …
-
Advanced Statistical Inference for Stochastic Quasi-Reaction Systems
… parameters can be estimated via a modified Expectation-Maximisation algorithm, with an extended Kalman filter at the E-step for reconstructing the underlying latent states. The approach is shown to be more accurate than existing methods, particularly for observations measured at short time …
-
Side scan sonar image formation, restoration and modelling.
… two iterative approaches based on the ICM and Expectation Maximisation (EM) algorithms are described.
-
A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration
… technical contributions to the field: i) An expectation maximisation-based principal component analysis with mutual information (EMPCA-MI) framework incorporating neighbourhood feature information; ii) Two innovative enhancements to reduce information redundancy and improve MI computational …
-
Longitudinal and survival statistical methods with applications in renal medicine
… estimated by maximum likelihood (ML) using an expectation-maximisation (EM) algorithm for the former model, by partial likelihood for the latter. Results show that Cox model underestimates the association parameter between the longitudinal and survival processes, and joint models correct this. …
-
Maximum likelihood parameter estimation in time series models using sequential Monte Carlo
… are based on batch and online versions of expectation-maximisation (EM) and gradient ascent, two widely popular algorithms for maximum likelihood estimation (MLE). In the last two decades, the range of statistical models where parameter estimation can be performed has been significantly …
-
Analysis of equity and interest rate returns in South Africa under the context of jump diffusion processes
… of Moments Estimation (MME) technique and the Expectation Maximisation (EM) algorithm. The calibration methods are applied to both simulated and empirical returns data. The simulation and empirical studies show that the standard MLE approach sometimes produces estimators which are not reliable …
-
A nested random effects model analysis of child survival in Malawi
… The parameters are also estimated using the expectation-maximisation (EM) algorithm, a method that has previously been used to analyse this type of data. Both approaches were implemented in Fortran using the NAG routines. The child survival data used in this study were collected as part of …
-
Edge-based motion segmentation
… and background) using two frames. The ExpectationMaximisation algorithm is used to determine the two motions and calculate the label probability for each edge. The frame is then segmented into regions. The best motion labelling for these regions is determined using simulated annealing. …
-
Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods
… inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and …
-
A new REML (PX)EM algorithm for linear mixed models and factor analytic mixed models
… failing to converge to a solution. The REML expectation maximisation (REML EM) algorithm and the parameter expanded version of this algorithm (REML PX EM) are alternatives to Newton-Raphson type algorithms. Features of these two algorithms are that the residual log-likelihood may not decrease …
-
A new REML (PX)EM algorithm for linear mixed models and factor analytic mixed models
… failing to converge to a solution. The REML expectation maximisation (REML EM) algorithm and the parameter expanded version of this algorithm (REML PX EM) are alternatives to Newton-Raphson type algorithms. Features of these two algorithms are that the residual log-likelihood may not decrease …
-
Disruption prediction at JET [Joint European Torus]
… and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. …
-
Longitudinal Joint Modelling Utilising the Coxian Phase-type Distribution
… typical survival processes. To this end, a new expectation-maximisation (EM) algorithm approach to fitting phase-type distributions is developed, shown through a simulation study to improve upon alternative algorithm approaches, employed within the current literature, in terms of both the …
-
Applications of cone beam computed tomography in radiotherapy treatment planning.
… was also investigated. Since the Ordered Subsets Expectation Maximisation (OSEM) package used in this study is intended for fan-beam geometry, only the slices from the central plane of cone-beam were chosen. The projections were corrected for distance-dependent resolution and centre of rotation …
-
Towards practical automated human action recognition
… components. When learning an HMM by way of Expectation-Maximisation (EM) algorithms, arbitrary choices must be made for their initial parameters. The initial choices have a major impact on the parameters at convergence and, in turn, on the recognition accuracy. This dependence forces us to …