Dissertation (PhD)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

In this thesis, we investigate two di erent approaches to animal movement modelling; nite mixture models, and di usion processes. These models are considered in two di erent contexts, rstly for analysis of data obtained in home range studies, and then, on a much smaller scale, modelling the movements of larvae. We consider the application of mixture models to home range movement data, and compare their performance with kernel density estimators commonly used for this purpose. Mixtures of bivariate normal distributions and bivariate t distributions are considered, and the latter are found to be good models for simulated and real movement data. The mixtures of bivariate t distributions are shown to provide a robust parametric approach. Subsequently, we investigate several measures of overlap for assessing site delity in home range data. Di usion processes for home range data are considered to model the tracks of animals. In particular, we apply models based on a bivariate Ornstein-Uhlenbeck process to recorded coyote movements. We then study modelling in a di erent application area involving tracks. Di usion models for the movements of larvae are used to investigate their behaviour when exposed to chemical compounds in a scienti c study. We nd that the tted models represent the movements of the larvae well, and correctly distinguish between the behaviour of larvae exposed to attractant and repellent compounds. Mixtures of di usion processes and Hidden Markov models provide more exible alternatives to single di usion processes, and are found to improve upon them considerably. A Hidden Markov model with 4 states is determined to be optimal, with states accounting for directed movement, localized movement and stationary observations. Models incorporating higherorder dependence are investigated, but are found to be less e ective than the use of multiple states for modelling the larvae movements.

No description available.

Precipitation is an important element in the hydrological cycle. To predict and simulate large-scale precipitation, Global Circulation Models (GCMs) are widely used. However, their grid scale is too big to apply to local hydrologic fields. In this study, non-homogenous hidden Markov models (NHMM) are explored as a means of generating the probability of precipitation occurrence in small scale given large-scaled weather patterns. Three different spatial models: (1) independent (2) auto-logistic (3) Chow-Liu tree, are also explored, along with methods and steps for parameter estimation. From this exploration, independent models with NHMM are recommended for very small precipitation networks, and the maximum likelihood method is found to be the most practical fitting method. If there are many points for downscaling, Chow-Liu tree models with the Expectation-Maximization (EM) algorithm are recommended. If more exact solutions are needed, auto-logistic models can be employed. If many points are considered in auto-logistic models, the (EM) algorithm should be used to estimate parameters separately and global optimization methods should be used for emission matrix. The major problem found with the NHMM model in this study is matching the rainfall amount for each year or month. This problem can be addressed by whether combining occurrence models with amount modes or by improving only occurrence models.

The Baum-Welch algorithm for training hidden Markov models (HMMs) requires model topology and initial parameters to be specifed, and iteratively improves the model parameters. Sometimes prior knowledge of the process being modeled allows such specifcation, but often this knowledge is unavailable. Experimentation and guessing are resorted to. Techniques for discovering the model structure from observation data exist but their use is not commonplace. We propose a state split-ting approach to structure discovery, where states are split based on two heuristics: within-state autocorrelation and a measure of Markov violation in the state path. Statistical hypothesis testing is used to decide which states to split, providing a natural termination criterion and taking into account the number of observations assigned to each state, splitting states only when the data demands it. / Thesis (M.A.)-University of KwaZulu-Natal, Durban, 2009.

Thesis (Ph. D.)--University of Aarhus, 2004. / Title from PDF title page (viewed on Jan 3, 2007). Includes articles and manuscripts co-authored with others. Includes bibliographical references. Also available in PostScript format.

No description available.

No description available.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 58 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 55-58).

Two brain segmentation approaches based on Hidden Markov Models are proposed. The first approach aims to segment normal brain 3D multi-channel MR images into three tissues WM, GM, and CSF. Linear Discriminant Analysis, LDA, is applied to separate voxels belonging to different tissues as well as to reduce their features vector size. The second approach aims to detect MS lesions in Brain 3D multi-channel MR images and to label WM, GM, and CSF tissues. Preprocessing is applied in both approaches to reduce the noise level and to address sudden intensity and global intensity correction. The proposed techniques are tested using 3D images from Montereal BrainWeb data set. In the first approach, the results were numerically assessed and compared to results reported using techniques based on single channel data and applied to the same data sets. The results obtained using the multi channel HMM-based algorithm were better than the results reported for single channel data in terms of an objective measure of overlap, Dice coefficient, compared to other methods. In the second approach, the segmentation accuracy is measured using Dice coefficient and total lesions load percentage