Bin Lu. / "September 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 118-126) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Lee Shing-yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 126-133). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.4 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Bayesian Networks --- p.7 / Chapter 2.1.1 --- A Simple Example [42] --- p.8 / Chapter 2.1.2 --- Formal Description and Notations --- p.9 / Chapter 2.1.3 --- Learning Bayesian Network from Data --- p.14 / Chapter 2.1.4 --- Inference on Bayesian Networks --- p.18 / Chapter 2.1.5 --- Applications of Bayesian Networks --- p.19 / Chapter 2.2 --- Bayesian Network Classifiers --- p.20 / Chapter 2.2.1 --- The Classification Problem in General --- p.20 / Chapter 2.2.2 --- Bayesian Classifiers --- p.21 / Chapter 2.2.3 --- Bayesian Network Classifiers --- p.22 / Chapter 2.3 --- Evolutionary Computation --- p.28 / Chapter 2.3.1 --- Four Kinds of Evolutionary Computation --- p.29 / Chapter 2.3.2 --- Cooperative Coevolution --- p.31 / Chapter 3 --- Bayesian Network Learning Algorithms --- p.33 / Chapter 3.1 --- Related Work --- p.34 / Chapter 3.1.1 --- Using GA --- p.34 / Chapter 3.1.2 --- Using EP --- p.36 / Chapter 3.1.3 --- Criticism of the Previous Approaches --- p.37 / Chapter 3.2 --- Two New Strategies --- p.38 / Chapter 3.2.1 --- A Hybrid Framework --- p.38 / Chapter 3.2.2 --- A New Operator --- p.39 / Chapter 3.3 --- CCGA --- p.44 / Chapter 3.3.1 --- The Algorithm --- p.45 / Chapter 3.3.2 --- CI Test Phase --- p.46 / Chapter 3.3.3 --- Cooperative Coevolution Search Phase --- p.47 / Chapter 3.4 --- HEP --- p.52 / Chapter 3.4.1 --- A Novel Realization of the Hybrid Framework --- p.54 / Chapter 3.4.2 --- Merging in HEP --- p.55 / Chapter 3.4.3 --- Prevention of Cycle Formation --- p.55 / Chapter 3.5 --- Summary --- p.56 / Chapter 4 --- Evaluation of Proposed Learning Algorithms --- p.57 / Chapter 4.1 --- Experimental Methodology --- p.57 / Chapter 4.2 --- Comparing the Learning Algorithms --- p.61 / Chapter 4.2.1 --- Comparing CCGA with MDLEP --- p.63 / Chapter 4.2.2 --- Comparing HEP with MDLEP --- p.65 / Chapter 4.2.3 --- Comparing CCGA with HEP --- p.68 / Chapter 4.3 --- Performance Analysis of CCGA --- p.70 / Chapter 4.3.1 --- Effect of Different α --- p.70 / Chapter 4.3.2 --- Effect of Different Population Sizes --- p.72 / Chapter 4.3.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.73 / Chapter 4.3.4 --- Effect of Varying Belief Factor --- p.76 / Chapter 4.4 --- Performance Analysis of HEP --- p.77 / Chapter 4.4.1 --- The Hybrid Framework and the Merge Operator --- p.77 / Chapter 4.4.2 --- Effect of Different Population Sizes --- p.80 / Chapter 4.4.3 --- Effect of Different --- p.81 / Chapter 4.4.4 --- Efficiency of the Merge Operator --- p.84 / Chapter 4.5 --- Summary --- p.85 / Chapter 5 --- Learning Bayesian Network Classifiers --- p.87 / Chapter 5.1 --- Issues in Learning Bayesian Network Classifiers --- p.88 / Chapter 5.2 --- The Multinet Classifier --- p.89 / Chapter 5.3 --- The Augmented Bayesian Network Classifier --- p.91 / Chapter 5.4 --- Experimental Methodology --- p.94 / Chapter 5.5 --- Experimental Results --- p.97 / Chapter 5.6 --- Discussion --- p.103 / Chapter 5.7 --- Application in Direct Marketing --- p.106 / Chapter 5.7.1 --- The Direct Marketing Problem --- p.106 / Chapter 5.7.2 --- Response Models --- p.108 / Chapter 5.7.3 --- Experiment --- p.109 / Chapter 5.8 --- Summary --- p.115 / Chapter 6 --- Conclusion --- p.116 / Chapter 6.1 --- Summary --- p.116 / Chapter 6.2 --- Future Work --- p.118 / Chapter A --- A Supplementary Parameter Study --- p.120 / Chapter A.1 --- Study on CCGA --- p.120 / Chapter A.1.1 --- Effect of Different α --- p.120 / Chapter A.1.2 --- Effect of Different Population Sizes --- p.121 / Chapter A.1.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.121 / Chapter A.1.4 --- Effect of Varying Belief Factor --- p.122 / Chapter A.2 --- Study on HEP --- p.123 / Chapter A.2.1 --- The Hybrid Framework and the Merge Operator --- p.123 / Chapter A.2.2 --- Effect of Different Population Sizes --- p.124 / Chapter A.2.3 --- Effect of Different Δα --- p.124 / Chapter A.2.4 --- Efficiency of the Merge Operator --- p.125

Leung Shek-hay. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 53-55). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Finite Mixtures of Structural Equation Model --- p.4 / Chapter Chapter 3 --- Bayesian Approach --- p.7 / Chapter Chapter 4 --- Two-stage Approach --- p.16 / Chapter Chapter 5 --- Simualtion Study --- p.22 / Chapter 5.1 --- Performance of the Two Approaches --- p.22 / Chapter 5.2 --- Influence of Prior Information of the Two Approaches --- p.26 / Chapter 5.3 --- Influence of the Component Probability to the Two Approaches --- p.28 / Chapter 5.4 --- Performance of the Two Approaches when the Components are not well-separated --- p.29 / Chapter Chapter 6 --- A Real Data Analysis --- p.31 / Chapter Chapter 7 --- Conclusion and Discussion --- p.35 / Appendix A Derviation of the Conditional Distribution --- p.37 / Appendix B Manifest Variables in the ICPSR Example --- p.39 / Appendix C A Sample LISREL Program for a Classified Group in the Simualtion Study --- p.40 / Appendix D A Sample LISREL Program for a Classified Group in the ICPSR Example --- p.41 / Tables 1-9 --- p.42 / Figures 1-2 --- p.51 / References --- p.53

Chung Po-Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 33-34). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Bayesian Approach --- p.4 / Chapter 2.1 --- Model Description --- p.5 / Chapter 2.2 --- Identification --- p.6 / Chapter 2.3 --- Bayesian Analysis of the Model --- p.8 / Chapter 2.3.1 --- Posterior Analysis --- p.8 / Chapter 2.3.2 --- The Gibbs Sampler --- p.9 / Chapter 2.3.3 --- Conditional Distributions --- p.10 / Chapter 2.4 --- Bayesian Estimation --- p.13 / Chapter 3 --- Two-stage Partition Approach --- p.15 / Chapter 3.1 --- First Stage: PRELIS --- p.15 / Chapter 3.2 --- Second Stage: LISREL --- p.17 / Chapter 3.2.1 --- Model Description --- p.17 / Chapter 3.2.2 --- Identification --- p.17 / Chapter 3.2.3 --- LISREL Analysis of the Model --- p.18 / Chapter 4 --- Comparison --- p.19 / Chapter 4.1 --- Simulation Studies --- p.19 / Chapter 4.2 --- Real Data Studies --- p.28 / Chapter 5 --- Conclusion & Discussion --- p.30 / Chapter A --- Tables for the Two Approaches --- p.35 / Chapter B --- Manifest variables in the ICPSR examples --- p.51 / Chapter C --- PRELIS & LISREL Scripts for Simulation Studies --- p.52

The insurance industry relies on both commercial and in-house software packages to quantify financial risk to natural hazards. For earthquakes, the initial loss estimates from the industry’s catastrophe risk (CAT) models are based on the probabilistic damage a building would sustain due to a catalog of simulated earthquake events. Based on the occurrence rates of the simulated earthquake events, an exceedance probability (EP) curve is calculated, which provides the probability of exceeding a specific loss threshold. Initially these loss exceedence probabilities help a company decide what insurance policies are most cost efficient. In addition they can also provide insights into loss predictions in the event that an actual natural disaster takes place, thus the insurance company is prepared to pay out their insured parties the necessary amount. However, there is always an associated uncertainty with the loss calculations produced by these models. The goal of this research is to reduce this uncertainty by using Bayesian inference with real time earthquake data to calculate an updated loss. Bayes theory is an iterative process that modifies the loss distribution with every piece of incoming information. The posterior updates are calculated by multiplying a baseline prior distribution with a likelihood function and normalization factor. The first prior is the initial loss distribution from the simulated events database before any information about a real earthquake is available. The crucial step in the update procedure is defining a likelihood function that establishes a relative weight for each simulated earthquake, relating how alike or dislike the attributes of a simulated earthquake are to those of a real earthquake event. To define this likelihood function, the general proposed approach is to quantify real time earthquake attributes such as magnitude, location, building tagging and damage, and compare them to an equivalent value for each simulated earthquake from the CAT model database. In order to obtain the simulated model parameters, the catastrophe risk model is analyzed for different building construction types, such as steel and reinforced concrete. For every model case, the loss, peak ground acceleration per building and simulated event magnitude and locations are recorded. Next, in order to calculate the real earthquake attributes, data was collected for three case studies, the 7.1 magnitude 1997 Punitaqui, the 8.8 magnitude 2010 Chile earthquake and the 6.7 magnitude 1994 Northridge earthquake. For each of these real earthquake events, the magnitude, location, peak ground acceleration at every available accelerometer location, building tagging and qualitative damage descriptions were recorded. Once the data was collected for both the real and simulated events, they were quantified so they could be compared on equal scales. Using the quantified parameter values, a likelihood function was defined for each update step. In general, as the number of updates increased, the loss estimates tended to converge to a steady value for both the medium and large event. In addition, the loss for the 6.7 and 7.1 event converged to a smaller value than that of the 8.8 event. The proposed methodology was only applied to earthquakes, but is broad enough to be applied to any type of peril.

Lau, Ka Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 46-48). / Abstract also in Chinese. / Chapter 1 --- Introduction to Global Credit Risk Management Standard --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 1.2 --- Basel Accords --- p.2 / Chapter 1.3 --- Risk Bucketing --- p.7 / Chapter 2 --- Current Practices of Risk Bucketing and PD Estimation --- p.10 / Chapter 2.1 --- Credit Scoring --- p.10 / Chapter 2.2 --- Risk Bucketing after Credit Scoring --- p.12 / Chapter 2.3 --- Related Literature Review --- p.14 / Chapter 2.4 --- Objective --- p.16 / Chapter 3 --- Bayesian Model for risk bucketing --- p.17 / Chapter 3.1 --- The Model --- p.17 / Chapter 3.2 --- Posterior Distribution --- p.19 / Chapter 3.3 --- Gibbs Sampler for the Posterior Distribution --- p.22 / Chapter 3.3.1 --- General Gibbs Sampler Theory --- p.22 / Chapter 3.3.2 --- The Gibbs Sampler for the Proposed Model --- p.23 / Chapter 3.4 --- Monitoring Convergence of the Gibbs Sampler --- p.26 / Chapter 3.5 --- "Estimation, Bucketing and Prediction" --- p.28 / Chapter 3.5.1 --- Estimation --- p.28 / Chapter 3.5.2 --- Bucketing --- p.28 / Chapter 3.5.3 --- Prediction --- p.29 / Appendix --- p.29 / Chapter 4 --- Simulation Studies and Real Data Analysis --- p.32 / Chapter 4.1 --- Simulation Studies --- p.32 / Chapter 4.1.1 --- Details of Simulation --- p.32 / Chapter 4.1.2 --- Simulation Procedures --- p.34 / Chapter 4.1.3 --- Predictive Performance --- p.35 / Chapter 4.1.4 --- Summary of Simulation Results --- p.36 / Chapter 4.2 --- Real Data Analysis --- p.37 / Chapter 5 --- Conclusion and Discussion --- p.44 / Bibliography --- p.46

Lam, Kwok Hap. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 68-72). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Two-level NSEM with outcomes from Exponential Family --- p.6 / Chapter 2.1 --- Basic Model Description --- p.7 / Chapter 2.2 --- Generalization from Normal Distribution to Exponential Family Distributions --- p.9 / Chapter 2.3 --- Bayesian Analysis of the Model --- p.10 / Chapter 2.3.1 --- Posterior Analysis and Gibbs Sampler --- p.10 / Chapter 2.3.2 --- Prior Distributions --- p.11 / Chapter 2.3.3 --- Bayesian Estimation --- p.13 / Chapter 2.3.4 --- Bayesian Model Selection --- p.14 / Chapter 2.4 --- A Simulation Study --- p.15 / Chapter 3 --- Two-level NSEM with mixed continuous and ordered categorical data --- p.28 / Chapter 3.1 --- Model Description --- p.29 / Chapter 3.2 --- Bayesian Analysis of the Model --- p.30 / Chapter 3.2.1 --- Posterior Analysis and Gibbs Sampler --- p.30 / Chapter 3.2.2 --- Bayesian Estimation --- p.31 / Chapter 3.3 --- A Simulation Study --- p.31 / Chapter 4 --- "Two-level NSEM with mixed continuous, count and binomial data" --- p.36 / Chapter 4.1 --- Model Description --- p.37 / Chapter 4.2 --- Bayesian Estimation --- p.38 / Chapter 4.3 --- A Simulation Study --- p.39 / Chapter 5 --- Two-level NSEM with mixed continuous and unordered categorical data --- p.43 / Chapter 5.1 --- Basic Model Description --- p.44 / Chapter 5.2 --- Bayesian Analysis of the Model --- p.47 / Chapter 5.2.1 --- Posterior Analysis and Gibbs Sampler --- p.47 / Chapter 5.2.2 --- Prior Distributions --- p.48 / Chapter 5.3 --- A Simulation Study --- p.49 / Chapter 6 --- Conclusion and Discussion --- p.53 / Chapter A --- Technical Details for Chapter 2 --- p.56 / Chapter A.1 --- Full conditional distributions --- p.56 / Chapter A.2 --- Implementation of the Metropolis-Hastings (MH) Algorithm --- p.59 / Chapter A.3 --- Gelman-Rubin statistic --- p.61 / Chapter B --- Technical Details for Chapter 3 --- p.63 / Chapter B.1 --- Full conditional distributions --- p.63 / Chapter B.2 --- Implementation of the Metropolis-Hastings (MH) Algorithm --- p.64 / Chapter C --- Technical Details for Chapter 5 --- p.66 / Chapter C.l --- Full conditional distributions --- p.66 / Bibliography --- p.68

Zhang, Qing. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 129-148). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Pan, Junhao. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 121-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

In population health, the study of unobserved, or latent, heterogeneity in longitudinal data may help inform public health interventions. Growth mixture modeling is a flexible tool for modeling latent heterogeneity in longitudinal data. However, the application of growth mixture models to certain data types, namely, complex survey data and electronic health records, is underdeveloped. For valid statistical inferences in complex survey data, features of the sample design must be incorporated into statistical analysis. In electronic health records, the application of growth mixture modeling is challenged by high levels of missing values. In this dissertation, I have three goals: First, I propose a Bayesian growth mixture model for complex survey data in which I directly incorporate features of the complex sample design. Second, I extend a Bayesian growth mixture model of multiple longitudinal health outcomes collected in electronic health records to a shared parameter model that can account for dierent missing data assumptions. Third, I develop open-source software packages in R for each method that can be used for model tting, selection, and checking.