by Chong-kan Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 97-103). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Related Work --- p.2 / Chapter 1.2 --- Organizations of the Dissertation --- p.4 / Chapter 1.3 --- Notations --- p.4 / Chapter 2 --- Overview of ICLP(R) --- p.6 / Chapter 2.1 --- Basics of Interval Arithmetic --- p.6 / Chapter 2.2 --- Relational Interval Arithmetic --- p.8 / Chapter 2.2.1 --- Interval Reduction --- p.8 / Chapter 2.2.2 --- Arithmetic Primitives --- p.10 / Chapter 2.2.3 --- Interval Narrowing and Interval Splitting --- p.13 / Chapter 2.3 --- Syntax and Semantics --- p.16 / Chapter 3 --- Limitations of Interval Narrowing --- p.18 / Chapter 3.1 --- Computation Inefficiency --- p.18 / Chapter 3.2 --- Inability to Detect Inconsistency --- p.23 / Chapter 3.3 --- The Newton Language --- p.27 / Chapter 4 --- Design of CIAL --- p.30 / Chapter 4.1 --- The CIAL Architecture --- p.30 / Chapter 4.2 --- The Inference Engine --- p.31 / Chapter 4.2.1 --- Interval Variables --- p.31 / Chapter 4.2.2 --- Extended Unification Algorithm --- p.33 / Chapter 4.3 --- The Solver Interface and Constraint Decomposition --- p.34 / Chapter 4.4 --- The Linear and the Non-linear Solvers --- p.37 / Chapter 5 --- The Linear Solver --- p.40 / Chapter 5.1 --- An Interval Gaussian Elimination Solver --- p.41 / Chapter 5.1.1 --- Naive Interval Gaussian Elimination --- p.41 / Chapter 5.1.2 --- Generalized Interval Gaussian Elimination --- p.43 / Chapter 5.1.3 --- Incrementality of Generalized Gaussian Elimination --- p.47 / Chapter 5.1.4 --- Solvers Interaction --- p.50 / Chapter 5.2 --- An Interval Gauss-Seidel Solver --- p.52 / Chapter 5.2.1 --- Interval Gauss-Seidel Method --- p.52 / Chapter 5.2.2 --- Preconditioning --- p.55 / Chapter 5.2.3 --- Increment ality of Preconditioned Gauss-Seidel Method --- p.58 / Chapter 5.2.4 --- Solver Interaction --- p.71 / Chapter 5.3 --- Comparisons --- p.72 / Chapter 5.3.1 --- Time Complexity --- p.72 / Chapter 5.3.2 --- Storage Complexity --- p.73 / Chapter 5.3.3 --- Others --- p.74 / Chapter 6 --- Benchmarkings --- p.76 / Chapter 6.1 --- Mortgage --- p.78 / Chapter 6.2 --- Simple Linear Simultaneous Equations --- p.79 / Chapter 6.3 --- Analysis of DC Circuit --- p.80 / Chapter 6.4 --- Inconsistent Simultaneous Equations --- p.82 / Chapter 6.5 --- Collision Problem --- p.82 / Chapter 6.6 --- Wilkinson Polynomial --- p.85 / Chapter 6.7 --- Summary and Discussion --- p.86 / Chapter 6.8 --- Large System of Simultaneous Equations --- p.87 / Chapter 6.9 --- Comparisons Between the Incremental and the Non-Incremental Preconditioning --- p.89 / Chapter 7 --- Concluding Remarks --- p.93 / Chapter 7.1 --- Summary and Contributions --- p.93 / Chapter 7.2 --- Future Work --- p.95 / Bibliography --- p.97

by Jones Chui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 62-[66]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Fundamentals of Artificial Neural Networks --- p.2 / Chapter 1.1.1 --- Processing Unit --- p.2 / Chapter 1.1.2 --- Topology --- p.3 / Chapter 1.1.3 --- Learning Rules --- p.4 / Chapter 1.2 --- Overview of Various Neural Network Models --- p.6 / Chapter 1.3 --- Introduction to the Radial Basis Function Networks (RBFs) --- p.8 / Chapter 1.3.1 --- Historical Development --- p.9 / Chapter 1.3.2 --- Some Intrinsic Problems --- p.9 / Chapter 1.4 --- Objective of the Thesis --- p.10 / Chapter 2 --- Low-dimensional Gaussian RBF networks (LowD RBFs) --- p.13 / Chapter 2.1 --- Architecture of LowD RBF Networks --- p.13 / Chapter 2.1.1 --- Network Structure --- p.13 / Chapter 2.1.2 --- Learning Rules --- p.17 / Chapter 2.2 --- Construction of LowD RBF Networks --- p.19 / Chapter 2.2.1 --- Growing Heuristic --- p.19 / Chapter 2.2.2 --- Pruning Heuristic --- p.27 / Chapter 2.2.3 --- Summary --- p.31 / Chapter 3 --- Application examples --- p.34 / Chapter 3.1 --- Chaotic Time Series Prediction --- p.35 / Chapter 3.1.1 --- Performance Comparison --- p.39 / Chapter 3.1.2 --- Sensitivity Analysis of MSE THRESHOLDS --- p.41 / Chapter 3.1.3 --- Effects of Increased Embedding Dimension --- p.41 / Chapter 3.1.4 --- Comparison with Tree-Structured Network --- p.46 / Chapter 3.1.5 --- Overfitting Problem --- p.46 / Chapter 3.2 --- Nonlinear prediction of speech signal --- p.49 / Chapter 3.2.1 --- Comparison with Linear Predictive Coding (LPC) --- p.54 / Chapter 3.2.2 --- Performance Test in Noisy Conditions --- p.55 / Chapter 3.2.3 --- Iterated Prediction of Speech --- p.59 / Chapter 4 --- Conclusion --- p.60 / Chapter 4.1 --- Discussions --- p.60 / Chapter 4.2 --- Limitations and Suggestions for Further Research --- p.61 / Bibliography --- p.62

Technological advances have enabled profiling gene expression variability, both at the RNA and the protein level, with ever increasing throughput. In addition, miniaturisation has enabled quantifying gene expression from small volumes of the input material and most recently at the level of single cells. Increasingly these technologies also preserve context information, such as assaying tissues with high spatial resolution. A second example of contextual information is multi-omics protocols, for example to assay gene expression and DNA methylation from the same cells or samples. Although such contextual gene expression datasets are increasingly available for both popu- lation and single-cell variation studies, methods for their analysis are not established. In this thesis, we propose two modelling approaches for the analysis of gene expression variation in specific biological contexts. The first contribution of this thesis is a statistical method for analysing single cell expression data in a spatial context. Our method identifies the sources of gene expression variability by decomposing it into different components, each attributable to a different source. These sources include aspects of spatial variation such as cell-cell interactions. In applications to data across different technologies, we show that cell-cell interactions are indeed a major determinant of the expression level of specific genes with a relevant link to their function. The second contribution is a latent variable model for the unsupervised analysis of gene expression data, while accounting for structured prior knowledge on experimental context. The proposed method enables the joint analysis of gene expression data and other omics data profiled in the same samples, and the model can be used to account for the grouping structure of samples, e.g. samples from individuals with different clinical covariates or from distinct experimental batches. Our model constitutes a principled framework to compare the molecular identities of these distinct groups.

Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Mathematics. / Vita. / Bibliography: leaves 111-114. / by Marjorie G. Hahn. / Ph.D.

No description available.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Computer Science." Includes bibliographical references (p. 79-83).

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

No description available.

No description available.