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Application of indicator kriging and conditional simulation in assessment of grade uncertainty in Hunters road magmatic sulphide nickel deposit in ZimbabweChiwundura, Phillip January 2017 (has links)
A research project report submitted to the Faculty of Engineering and the Built
Environment, University of the Witwatersrand, in fulfilment of the requirements
for the degree of Masters of Science in Engineering, 2017 / The assessment of local and spatial uncertainty associated with a
regionalised variable such as nickel grade at Hunters Road magmatic
sulphide deposit is one of the critical elements in the resource estimation.
The study focused on the application of Multiple Indicator Kriging (MIK) and
Sequential Gaussian Simulation (SGS) in the estimation of recoverable
resources and the assessment of grade uncertainty at Hunters Road’s
Western orebody. The Hunters Road Western orebody was divided into two
domains namely the Eastern and the Western domains and was evaluated
based on 172 drill holes. MIK and SGS were performed using Datamine
Studio RM module. The combined Mineral Resources estimate for the
Western orebody at a cut-off grade of 0.40%Ni is 32.30Mt at an average
grade of 0.57%Ni, equivalent to 183kt of contained nickel metal. SGS
results indicated low uncertainty associated with Hunters Road nickel
project with 90% probability of an average true grade above cut-off, lying
within +/-3% of the estimated block grade. The estimate of the mean based
on SGS was 0.55%Ni and 0.57% Ni for the Western and Eastern domains
respectively. MIK results were highly comparable with SGS E-type
estimates while the most recent Ordinary Kriging (OK) based estimates by
BNC dated May 2006, overstated the resources tonnage and
underestimated the grade compared to the MIK estimates. It was concluded
that MIK produced better estimates of recoverable resources than OK.
However, since only E-type estimates were produced by MIK, post
processing of “composite” conditional cumulative distribution function (ccdf)
results using a relevant change of support algorithm such as affine
correction is recommended. Although SGS produced a good measure of
uncertainty around nickel grades, post processing of realisations using a
different software such as Isatis has been recommended together with
combined simulation of both grade and tonnage. / XL2018
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Effects of gene selection and data sampling on prediction of breast cancer treatmentsUnknown Date (has links)
In recent years more and more researchers have begun to use data mining and
machine learning tools to analyze gene microarray data. In this thesis we have collected a
selection of datasets revolving around prediction of patient response in the specific area
of breast cancer treatment. The datasets collected in this paper are all obtained from gene
chips, which have become the industry standard in measurement of gene expression. In
this thesis we will discuss the methods and procedures used in the studies to analyze the
datasets and their effects on treatment prediction with a particular interest in the selection
of genes for predicting patient response. We will also analyze the datasets on our own in
a uniform manner to determine the validity of these datasets in terms of learning potential
and provide strategies for future work which explore how to best identify gene signatures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection
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Contribuições da teoria da estimação para modulações digitais que utilizam sinais caóticos. / Contributions of the estimation theory to digital modulations that use chaotic signals.Eisencraft, Marcio 17 April 2006 (has links)
Neste trabalho investiga-se o emprego de técnicas de estimação em sistemas de modulação digital que utilizam sinais caóticos. Inicialmente, aspectos básicos das teorias de sistemas não-lineares e de modulações digitais são revisitados seguidos de técnicas recentemente propostas de modulações digitais caóticas com receptores por correlação coerente, não-coerente e diferencial: o CSK (Chaos Shift Keying), o DCSK (Differential Chaos Shift Keying) e algumas de suas variantes, em especial o FM-DCSK (Frequency Modulated DCSK). Nessa descrição, utiliza-se a notação de equivalente passa-baixas de tempo discreto para facilitar a comparação com modulações digitais convencionais. Deduz-se o limite de Cramér-Rao para a estimação da condição inicial de órbitas caóticas em função de propriedades estatísticas do mapa que as gerou e descrevem-se dois estimadores para elas: o MLE (Maximum Likelihood Estimator) que se aplica a mapas com densidade invariante uniforme e o algoritmo de Viterbi para o qual se apresenta uma generalização a fim de aplicá-lo a uma classe maior de mapas. Por apresentar ganho de estimação maior na faixa de relação sinal-ruído de interesse, este último é utilizado em propostas de sistemas de modulação digital que utilizam estimação de órbitas para detectar o símbolo enviado: o ML-CSK (Maximum Likelihood CSK) modificado para poder usar mapas com densidade invariante não-uniforme, empregando um ou dois mapas e o ML-DCSK (Maximum Likelihood DCSK). Por simulação, avaliou-se o desempenho em termos de taxa de erro desses sistemas sob ruído branco aditivo gaussiano. / In this work, we investigate the use of estimation techniques to digital modulation systems that use chaotic signals. Initially, basic aspects of nonlinear systems and digital modulation theory are reviewed followed by currently proposed techniques of chaotic digital modulation with coherent, noncoherent and differential correlation receivers: CSK (Chaos Shift Keying), DCSK (Differential Chaos Shift Keying) and some of its variants in special FM-DCSK (Frequency Modulated DCSK). These systems are described using a discrete-time lowpass equivalent model to facilitate comparison with conventional digital modulation systems. We derive Cramér-Rao lower bounds for the estimation of the initial condition of chaotic orbits as a function of the statistical properties of the chaos generating map and describe two chaotic orbits estimators: the MLE (Maximum Likelihood Estimator) that applies only to maps with uniform invariant density and the Viterbi algorithm for which a generalization is presented that allows its application to a broader class of maps. Because of the larger estimation gains attained in the signal-to-noise ratio range of interest, the latter is used in proposed digital modulation systems that use orbit estimation to detect the transmitted symbol: ML-CSK (Maximum Likelihood CSK) modified to allow maps with nonuniform invariant density using one map or two maps and ML-DCSK (Maximum Likelihood DCSK). The performance of these systems in terms of symbol error rate is accessed via simulation under additive white gaussian noise perturbations.
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Bootstrap standard error and confidence intervals for the correlation corrected for indirect range restriction: a Monte Carlo study. / Bootstrap method / Bootstrap standard error & confidence intervals for the correlation corrected for indirect range restrictionJanuary 2006 (has links)
Li Johnson Ching Hong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 40-42). / Abstracts in English and Chinese. / ACKNOWLEDGEMENT --- p.2 / ABSTRACT --- p.3 / CHINESE ABSTRACT --- p.4 / TABLE OF CONTENTS --- p.5 / Chapter CHAPTER 1 --- INTRODUCTION --- p.7 / Thorndike's Three Formulae to Correct Correlation for Range Restriction --- p.8 / Significance of Case 3 --- p.9 / Importance of Standard Error and Confidence Intervals --- p.10 / Research Gap in the Estimation of Standard Error of rc and the Construction of the Confidence Intervals for pxy --- p.10 / Objectives of the Present Study --- p.12 / Chapter CHAPTER 2 --- BOOTSTRAP METHOD --- p.13 / Different Confidence Intervals Constructed for the Present Study --- p.14 / Chapter CHAPTER 3 --- A PROPOSED PROCEDURE FOR THE ESTIMATION OF STANDAR ERROR OF rc AND THE CONSTRUCTION OF CONFIDENCE INTERVALS --- p.16 / Chapter CHAPTER 4 --- METHODS --- p.20 / Model Specifications --- p.20 / Procedure --- p.21 / Chapter CHAPTER 5 --- ASSESSMENT --- p.23 / Chapter CHAPTER 6 --- RESULTS --- p.25 / Accuracy of Average Correlation Corrected for IRR ( rc ) --- p.25 / Empirical Standard Deviation (SDE) of rc --- p.29 / Accuracy of Standard Error Estimate --- p.29 / Accuracy of Confidence Intervals --- p.33 / Chapter CHAPTER 7 --- DISCUSSION AND CONCLUSION --- p.36 / Chapter CHAPTER 8 --- LIMITATIONS AND FURTHER DIRECTIONS --- p.38 / REFERENCES --- p.40 / APPENDIX A --- p.43 / FIGURE CAPTION --- p.53 / LIST OF TABLES --- p.55
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Improved estimation of the scale matrix in a one-sample and two-sample problem.January 1998 (has links)
by Foon-Yip Ng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 111-115). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Main Problems --- p.1 / Chapter 1.2 --- The Basic Concept of Decision Theory --- p.4 / Chapter 1.3 --- The Class of Orthogonally Invariant Estimators --- p.6 / Chapter 1.4 --- Related Works --- p.8 / Chapter 1.5 --- Summary --- p.10 / Chapter Chapter 2 --- Estimation of the Scale Matrix in a Wishart Distribution --- p.12 / Chapter 2.1 --- Review of the Previous Works --- p.13 / Chapter 2.2 --- Some Useful Statistical and Mathematical Results --- p.15 / Chapter 2.3 --- Improved Estimation of Σ under the Loss L1 --- p.18 / Chapter 2.4 --- Simulation Study for Wishart Distribution under the Loss L1 --- p.22 / Chapter 2.5 --- Improved Estimation of Σ under the Loss L2 --- p.25 / Chapter 2.6 --- Simulation Study for Wishart Distribution under the Loss L2 --- p.28 / Chapter Chapter 3 --- Estimation of the Scale Matrix in a Multivariate F Distribution --- p.31 / Chapter 3.1 --- Review of the Previous Works --- p.32 / Chapter 3.2 --- Some Useful Statistical and Mathematical Results --- p.35 / Chapter 3.3 --- Improved Estimation of Δ under the Loss L1____ --- p.38 / Chapter 3.4 --- Simulation Study for Multivariate F Distribution under the Loss L1 --- p.42 / Chapter 3.5 --- Improved Estimation of Δ under the Loss L2 ________ --- p.46 / Chapter 3.6 --- Relationship between Wishart Distribution and Multivariate F Distribution --- p.51 / Chapter 3.7 --- Simulation Study for Multivariate F Distribution under the Loss L2 --- p.52 / Chapter Chapter 4 --- Estimation of the Scale Matrix in an Elliptically Contoured Matrix Distribution --- p.57 / Chapter 4.1 --- Some Properties of Elliptically Contoured Matrix Distributions --- p.58 / Chapter 4.2 --- Review of the Previous Works --- p.60 / Chapter 4.3 --- Some Useful Statistical and Mathematical Results --- p.62 / Chapter 4.4 --- Improved Estimation of Σ under the Loss L3 --- p.63 / Chapter 4.5 --- Simulation Study for Multivariate-Elliptical t Distributions under the Loss L3 --- p.67 / Chapter 4.5.1 --- Properties of Multivariate-Elliptical t Distribution --- p.67 / Chapter 4.5.2 --- Simulation Study for Multivariate- Elliptical t Distributions --- p.70 / Chapter 4.6 --- Simulation Study for ε-Contaminated Normal Distributions under the Loss L3 --- p.74 / Chapter 4.6.1 --- Properties of ε-Contaminated Normal Distributions --- p.74 / Chapter 4.6.2 --- Simulation Study for 2-Contaminated Normal Distributions --- p.76 / Chapter 4.7 --- Discussions --- p.79 / APPENDIX --- p.81 / BIBLIOGRAPHY --- p.111
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Searching for the contemporary and temporal causal relations from data. / 数据中的时间因果关联分析 / CUHK electronic theses & dissertations collection / Shu ju zhong de shi jian yin guo guan lian fen xiJanuary 2012 (has links)
因果分析由于可以刻画随机事件之间的关系而被关注,而图模型则是描述因果关系的重要工具。在图模型框架中,数据集中隐含的因果关系被表示为定义在这个数据集上的贝叶斯网络,通过贝叶斯网络学习就可以完成数据集上的因果关系挖掘。因此,贝叶斯网络学习在因果分析中具有非常重要的作用。在本文中,我们提出了一种二段式的贝叶斯网络学习算法。在第一阶段,此算法从数据中构建出马尔可夫随机场。在第二阶段,此算法根据学习到的条件随机场构造出贝叶斯网络。本文中提出的二段式贝叶斯网络学习算法具有比现有算法更高的准确率,而且这种二段式算法中的一些技术可以很容易的被应用于其他贝叶斯网络学习算法当中。此外,通过与其他的时间序列中的因果分析模型(例如向量自回归和结构向量自回归模型)做比较,我们可以看出二段式的贝叶斯网络学习算法可以被用于时间序列的因果分析。 通过在真实数据集上的实验,我们证明的二段式贝叶斯网络学习算法在实际问题中的可用性。 / 本文开始介绍了基于约束的贝叶斯网络学习框架,其中的代表作是SGS 算法。在基于约束的贝叶斯网络学习框架中,如何减小测试条件独立的搜索空间是提高算法性能的关键步骤。二段式贝叶斯网络学习算法的核心即是研究如何减小条件独立测试的搜索空间。为此,我们证明了通过马尔可夫随机场来确定贝叶斯网络的结构可以有效的减小条件独立测试的计算复杂性以及增加算法的稳定性。在本文中,偏相关系数被用来度量条件独立。这种方法可用于基于约束的贝叶斯网络学习算法。具体来说,本文证明了在给定数据集的生成模型为线性的条件下,偏相关系数可被用于度量条件独立。而且本文还证明了高斯模型是线性结构方程模型的一个特例。本文比较了二段式的贝叶斯网络学习算法与当前性能最佳的贝叶斯算法在一系列真实贝叶斯网络上的表现。 / 文章的最后一部分研究了二段式的贝叶斯网络学习算法在时间序列因果分析中的应用。在这部分工作中,我们首先证明了结构向量自回归模型模型在高斯过程中不能发现同时期的因果关系。失败的原因是结构向量自回归模型不能满足贝叶斯网络的忠实性条件。因此,本文的最后一部分提出了一种区别于现有工作的基于贝叶斯网络的向量自回归和结构向量自回归模型学习算法。并且通过实验证明的算法在实际问题中的可用性。 / Causal analysis has drawn a lot of attention because it provides with deep insight of relations between random events. Graphical model is a dominant tool to represent causal relations. Under graphical model framework, causal relations implied in a data set are captured by a Bayesian network defined on this data set and causal discovery is achieved by constructing a Bayesian network from the data set. Therefore, Bayesian network learning plays an important role in causal relation discovery. In this thesis, we develop a Two-Phase Bayesian network learning algorithm that learns Bayesian network from data. Phase one of the algorithm learns Markov random fields from data, and phase two constructs Bayesian networks based on Markov random fields obtained. We show that the Two-Phase algorithm provides state-of-the-art accuracy, and the techniques proposed in this work can be easily adopted by other Bayesian network learning algorithms. Furthermore, we present that Two-Phase algorithm can be used for time series analysis by evaluating it against a series of time series causal learning algorithms, including VAR and SVAR. Its practical applicability is also demonstrated through empirical evaluation on real world data set. / We start by presenting a constraint-based Bayesian network learning framework that is a generalization of SGS algorithm [86]. We show that the key step in making Bayesian networks to learn efficiently is restricting the search space of conditioning sets. This leads to the core of this thesis: Two-Phase Bayesian network learning algorithm. Here we show that by learning Bayesian networks fromMarkov random fields, we efficiently reduce the computational complexity and enhance the reliability of the algorithm. Besides the proposal of this Bayesian network learning algorithm, we use zero partial correlation as an indicator of conditional independence. We show that partial correlation can be applied to arbitrary distributions given that data are generated by linear models. In addition, we prove that Gaussian distribution is a special case of linear structure equation model. We then compare our Two-Phase algorithm to other state-of-the-art Bayesian network algorithms on several real world Bayesian networks that are used as benchmark by many related works. / Having built an efficient and accurate Bayesian network learning algorithm, we then apply the algorithm for causal relation discovering on time series. First we show that SVAR model is incapable of identifying contemporaneous causal orders for Gaussian process because it fails to discover the structures faithful to the underlying distributions. We also develop a framework to learn true SVAR and VAR using Bayesian network, which is distinct from existing works. Finally, we show its applicability to a real world problem. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Zhenxing. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 184-195). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Causal Relation and Directed Graphical Model --- p.1 / Chapter 1.2 --- A Brief History of Bayesian Network Learning --- p.3 / Chapter 1.3 --- Some Important Issues for Causal BayesianNetwork Learning --- p.5 / Chapter 1.3.1 --- Learning Bayesian network locally --- p.6 / Chapter 1.3.2 --- Conditional independence test --- p.7 / Chapter 1.3.3 --- Causation discovery for time series --- p.8 / Chapter 1.4 --- Road Map of the Thesis --- p.10 / Chapter 1.5 --- Summary of the Remaining Chapters --- p.12 / Chapter 2 --- Background Study --- p.14 / Chapter 2.1 --- Notations --- p.14 / Chapter 2.2 --- Formal Preliminaries --- p.15 / Chapter 2.3 --- Constraint-Based Bayesian Network Learning --- p.24 / Chapter 3 --- Two-Phase Bayesian Network Learning --- p.33 / Chapter 3.1 --- Two-Phase Bayesian Network Learning Algorithm --- p.35 / Chapter 3.1.1 --- Basic Two-Phase algorithm --- p.37 / Chapter 3.1.2 --- Two-Phase algorithm with Markov blanket information --- p.59 / Chapter 3.2 --- Correctness Proof and Complexity Analysis --- p.73 / Chapter 3.2.1 --- Correctness proof --- p.73 / Chapter 3.2.2 --- Complexity analysis --- p.81 / Chapter 3.3 --- Related Works --- p.83 / Chapter 3.3.1 --- Search-and-score algorithms --- p.84 / Chapter 3.3.2 --- Constraint-based algorithms --- p.85 / Chapter 3.3.3 --- Other algorithms --- p.86 / Chapter 4 --- Measuring Conditional Independence --- p.88 / Chapter 4.1 --- Formal Definition of Conditional Independence --- p.88 / Chapter 4.2 --- Measuring Conditional Independence --- p.96 / Chapter 4.2.1 --- Measuring independence with partial correlation --- p.96 / Chapter 4.2.2 --- Measuring independence with mutual information --- p.104 / Chapter 4.3 --- Non-Gaussian Distributions and Equivalent Class --- p.108 / Chapter 4.4 --- Heuristic CI Tests UnderMonotone Faithfulness Condition --- p.116 / Chapter 5 --- Empirical Results of Two-Phase Algorithms --- p.125 / Chapter 5.1 --- Experimental Setup --- p.126 / Chapter 5.2 --- Structure Error After Each Phase of Two-Phase Algorithms --- p.129 / Chapter 5.3 --- Maximal and Average Sizes of Conditioning Sets --- p.131 / Chapter 5.4 --- Comparison of the Number of CI Tests Required by Dependency Analysis Approaches --- p.133 / Chapter 5.5 --- Reason forWhich Number of CI Tests Required Grow with Sample Size --- p.135 / Chapter 5.6 --- Two-Phase Algorithms on Linear Gaussian Data --- p.136 / Chapter 5.7 --- Two-phase Algorithms on Linear Non-Gaussian Data --- p.139 / Chapter 5.8 --- Compare Two-phase Algorithms with Search-and-Score Algorithms and Lasso Regression --- p.142 / Chapter 6 --- Causal Mining in Time Series Data --- p.146 / Chapter 6.1 --- A Brief Review of Causation Discovery in Time Series --- p.146 / Chapter 6.2 --- Limitations of Constructing SVAR from VAR --- p.150 / Chapter 6.3 --- SVAR Being Incapability of Identifying Contemporaneous Causal Order for Gaussian Process --- p.152 / Chapter 6.4 --- Estimating the SVARs by Bayesian Network Learning Algorithm --- p.157 / Chapter 6.4.1 --- Represent SVARs by Bayesian networks --- p.158 / Chapter 6.4.2 --- Getting back SVARs and VARs fromBayesian networks --- p.159 / Chapter 6.5 --- Experimental Results --- p.162 / Chapter 6.5.1 --- Experiment on artificial data --- p.162 / Chapter 6.5.2 --- Application in finance --- p.172 / Chapter 6.6 --- Comparison with Related Works --- p.174 / Chapter 7 --- Concluding Remarks --- p.178 / Bibliography --- p.184
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Statistical inferences for a pure birth processHsu, Jyh-Ping January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
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Modeling and estimation of space-time stochastic processes.Kam, Pooi Yuen January 1977 (has links)
Thesis. 1977. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Engineering. / Includes bibliographical references. / Ph.D.
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Enhancement and bandwidth compression of noisy speech by estimation of speech and its model parameters.Lim, Jae Soo January 1978 (has links)
Thesis. 1978. Sc.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Includes bibliographical references. / Sc.D.
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Structural properties and estimation of delay systems.Kwong, Raymond Hon-Sing January 1975 (has links)
Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Vita. / Includes bibliographical references. / Ph.D.
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