21 |
The transition between quenched and annealed exponents in two-dimensional quantum gravityDias Correia, Joao Luis Goncalves January 1998 (has links)
No description available.
|
22 |
Cell based models of tumour angiogenesisPlank, Michael John January 2003 (has links)
No description available.
|
23 |
On some problems related to machine-generated noiseStockis, Jean-Pierre January 1997 (has links)
No description available.
|
24 |
Random-bonds Ising models, quantum localisation and critical behaviour in two dimensionsMerz, Florian January 2002 (has links)
No description available.
|
25 |
Problems concerning the diffusion of more than one rumourOsei, Gibson Kwame January 1976 (has links)
No description available.
|
26 |
Using random matrix theory to determine the intrinsic dimension of a hyperspectral imageCawse-Nicholson, Kerry 04 February 2013 (has links)
Determining the intrinsic dimension of a hyperspectral image is an important step in the
spectral unmixing process, since under- or over- estimation of this number may lead to
incorrect unmixing for unsupervised methods. In this thesis we introduce a new method
for determining the intrinsic dimension, using recent advances in Random Matrix Theory
(RMT). This method is not sensitive to non-i.i.d. and correlated noise, and it is entirely
unsupervised and free from any user-determined parameters. The new RMT method is
mathematically derived, and robustness tests are run on synthetic data to determine how
the results are a ected by: image size; noise levels; noise variability; noise approximation;
spectral characteristics of the endmembers, etc. Success rates are determined for many
di erent synthetic images, and the method is compared to two principal state of the
art methods, Noise Subspace Projection (NSP) and HySime. All three methods are
then tested on twelve real hyperspectral images, including images acquired by satellite,
airborne and land-based sensors. When images that were acquired by di erent sensors
over the same spatial area are evaluated, RMT gives consistent results, showing the
robustness of this method to sensor characterisics.
|
27 |
Analysis of censored and polytomous data.January 1992 (has links)
by Wai-kuen Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 57). / Chapter Chapter 1 : --- Introduction --- p.1 / Chapter Chapter 2 : --- Estimation of Correlation between Censored and Polytomous Variables --- p.5 / Chapter 2.1 : --- Model --- p.5 / Chapter 2.2 : --- Maximum Likelihood Estimation between a Censored and a Polytomous Variable --- p.7 / Chapter 2.3 : --- Simulation Study --- p.14 / Chapter 2.4 : --- Extension to Several Variables --- p.18 / Chapter Chapter 3 : --- An application -- Correlation Structure Analysis --- p.33 / Chapter 3.1 : --- Model --- p.33 / Chapter 3.2 : --- Two-stage Estimation Procedure --- p.35 / Chapter 3.3 : --- Optimization Procedure --- p.37 / Chapter Chapter 4 : --- Conclusion --- p.40 / Tables --- p.42 / References --- p.57
|
28 |
Bayesian analysis of stochastic constraints in structural equation model with polytomous variables in serveral groups.January 1990 (has links)
by Tung-lok Ng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 57-59. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Full Maximum Likelihood Estimation of the General Model --- p.4 / Chapter 2.1 --- Introduction --- p.4 / Chapter 2.2 --- Model --- p.4 / Chapter 2.3 --- Identification of the model --- p.5 / Chapter 2.4 --- Maximum likelihood estimation --- p.7 / Chapter 2.5 --- Computational Procedure --- p.12 / Chapter 2.6 --- Tests of Hypothesis --- p.13 / Chapter 2.7 --- Example --- p.14 / Chapter Chapter 3 --- Bayesian Analysis of Stochastic Prior Information --- p.17 / Chapter 3.1 --- Introduction --- p.17 / Chapter 3.2 --- Bayesian Analysis of the general model --- p.18 / Chapter 3.3 --- Computational Procedure --- p.22 / Chapter 3.4 --- Test the Compatibility of the Prior Information --- p.24 / Chapter 3.5 --- Example --- p.25 / Chapter Chapter 4 --- Simulation Study --- p.27 / Chapter 4.1 --- Introduction --- p.27 / Chapter 4.2 --- Simulation1 --- p.27 / Chapter 4.3 --- Simulation2 --- p.30 / Chapter 4.4 --- Summary and Discussion --- p.31 / Chapter Chapter 5 --- Concluding Remarks --- p.33 / Tables / References --- p.57
|
29 |
Covariance structure analysis with polytomous and interval data.January 1992 (has links)
by Yin-Ping Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 95-96). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of the Correlation between Polytomous and Interval Data --- p.6 / Chapter 2.1 --- Model --- p.6 / Chapter 2.2 --- Maximum Likelihood Estimation --- p.8 / Chapter 2.3 --- Partition Maximum Likelihood Estimation --- p.10 / Chapter 2.4 --- Optimization Procedure and Simulation Study --- p.18 / Chapter Chapter 3 --- Three-stage Procedure for Covariance Structure Analysis --- p.25 / Chapter 3.1 --- Model --- p.25 / Chapter 3.2 --- Three-stage Estimation Method --- p.26 / Chapter 3.3 --- Optimization Procedure and Simulation Study --- p.38 / Chapter Chapter 4 --- Two-stage Procedure for Correlation Structure Analysis --- p.46 / Chapter 4.1 --- Model --- p.47 / Chapter 4.2 --- Two-stage Estimation Method --- p.47 / Chapter 4.3 --- Optimization Procedure and Monte Carlo Study --- p.50 / Chapter 4.4 --- Comparison of Two Methods --- p.53 / Chapter Chapter 5 --- Conclusion --- p.56 / Tables --- p.58 / References --- p.95
|
30 |
Estimation of correlations between truncated continuous and polytomous variables.January 1994 (has links)
by Wai-chung Lui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 76-82). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of the model with one truncated continuous variable and one polytomous variable --- p.6 / Chapter §2.1 --- The model / Chapter § 2.2 --- Likelihood function of the model / Chapter § 2.3 --- Derivatives of F (θ) / Chapter § 2.4 --- Asymptotic properties of the model / Chapter Chapter 3 --- Estimation of the model with one truncated continuous variable and several polytomous variables --- p.22 / Chapter § 3.1 --- The model / Chapter § 3.2 --- Partition Maximum Likelihood (PML) estimation / Chapter § 3.3 --- Asymptotic properties of the PML estimates / Chapter Chapter 4 --- Optimization procedures and Simulation study --- p.43 / Chapter § 4.1 --- Optimization procedures / Chapter § 4.2 --- Simulation study / Chapter Chapter 5 --- Summary and Conclusion --- p.54 / Tables --- p.56 / References --- p.76
|
Page generated in 0.0561 seconds