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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭克聲(Ke-Sheng Cheng) | |
| dc.contributor.author | Jun-Jih Liou | en |
| dc.contributor.author | 劉俊志 | zh_TW |
| dc.date.accessioned | 2021-06-14T16:46:15Z | - |
| dc.date.available | 2008-08-06 | |
| dc.date.copyright | 2008-08-06 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-31 | |
| dc.identifier.citation | Chapter 1
[1] Bell, E.J., Hinojosa, R.C. (1977), “Markov analysis of land use change: continuous time and stationary processes”, Socio-Economic Planning Science 11, 13–17. [2] Chapple, P.B., Bertilone, D.C. (1998), “Stochastic simulation of infrared non-Gaussian natural terrain imagery”, Optics Communications 150, 71–76. [3] Cheng, K.S., Lei, T.C. (2001), “Reservoir trophic state evaluation using Landsat TM images”, Journal of the American Water Resources Association 37(5), 1321–1334. [4] Cheng, K.S., Lin, Y.C., Liou, J.J. (2008a), “Raingauge network evaluation and augmentation using geostatistics”, Hydrological Processes. (in press) [5] Cheng, K.S., Hou, J.J., Liou, J.J. (2008b), “Stochastic simulation of bivariate gamma distribution – a frequency-factor based approach”, Environmetrics. (in review) [6] Cheng, K.S., Hueter, I., Hsu, E.C., Yeh, H.C. (2001), “A scale-invariant Gauss-Markov model for design storm hyetographs”, Journal of the American Water Resources Association 37(3), 723–736. [7] Cheng, K.S., Wei, C., Cheng, Y.B., Yeh, H.C. (2003), “Effect of spatial variation characteristics on contouring of design storm depth”, Hydrological Processes 17, 1755–1769. [8] Cheng, K.S., Yeh, H.C., Tsai, C.H. (2000), “An anisotropic spatial modeling approach for remote sensing image rectification”, Remote Sensing of Environment 73(1), 46–54. [9] Cheng, K.S., Yeh, H.C., Liou, C.Y. (2000), “A comparative study of drought prediction techniques for reservoir operation”, Journal of the American Water Resources Association 36(3), 511–521. [10] Christakos, G. (1992), Random Field Models in Earth Sciences. San Diego, Academic Press, Inc. [11] Elfeki, A., Dekking, M. (2001), “A Markov chain model for subsurface characterization: theory and applications”, Mathematical Geology 33(5), 569–589. [12] Emery, X. (2007), “Using the Gibbs sampler for conditional simulation of Gaussian-based random fields”, Computers and Geosciences 33, 522–537. [13] Jang, C.S., Liu, C.W. (2004), “Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan, Taiwan”, Hydrological Processes 18, 1333–1350. [14] Jung, M., Crawford, M.M. (2003), “Model based simulation of multispectral images based on remotely sensed data”, Simulation Modelling Practice and Theory 11, 151–169. [15] Lee, Y.M., Ellis, J.H. (1997), “Estimation and simulation of lognormal random fields”, Computers and Geosciences 23(1), 19–31. [16] Li, W., Zhang, C., Burt, J.E., Zhu, A.X., Feyen, J. (2004), “Two-dimensional Markov chain simulation of soil type spatial distribution”, Soil Science of America Journal 68, 1479–1490. [17] Lin, Y.P., Chang, T.K. (2000), “Geostatistical simulation and estimation of the spatial variability of soil zinc”, Journal of Environmental Science and Health, Part A - Toxic/Hazardous Substance & Environmental Engineering A 35(3), 327–347. [18] Lin Y.P., Chang, T.K., Teng, T.P. (2001), “Characterization of soil lead by comparing of sequential Gaussian simulation, simulated annealing simulation and kriging methods”, Environmental Geology 41(1/2), 189–199. [19] Mardia, K.V. (1988), “Multi-dimensional multivariate Gaussian Markov random fields with application to image processing”, Journal of Multivariate Analysis 24, 265–284. [20] Onof, C., Chandler, R.E., Kakou, A., Northrop, P., Wheater H.S., and Isham, V. (2000), “Rainfall modeling using poisson-cluster processes: a review of developments”, Stochastic Environmental Research and Risk Assessment 14, 384–411. [21] Rue, H., Tjelmeland, H. (2002), “Fitting Gaussian Markov random fields to Gaussian fields”, Scandinavian Journal of Statistics 29, 31–49. [22] Schnabel, U., Tietje, O., and Scholz, R.W. (2004), “Uncertainty assessment for management of soil contaminants with sparse data”, Environmental Management 33(6), 911–925. [23] Teng, S.P., Chen, Y.K., Cheng, K.S., Lo, H.C. (2008), “Hypothesis-test-based landcover change detection using multitemporal satellite images”, Advances in Space Research. (in press) [24] Vose, D. (2000), Risk Analysis: a quantitative guide. Second edition, John Wiley & Sons, Ltd. [25] Yue, S., Ouarda, T.B.M.J., Bobee, B. (2001), “A review of bivariate gamma distributions for hydrological application”, Journal of Hydrology 246, 1–18. [26] Zhang, J., Goodchild, M.F. (2002), Uncertainty in Geological Information. London; New York, Taylor & Francis. Chapter 2 [1] Andrews, D.F., Gnanadesikan, R., Warner, J.L. (1973), Methods of assessing multivariate normality. In: Krishnaiah, P.R. (Ed.), Multivariate analysis. Academic Press, New York. [2] Bowman, K.O., Shenton, L.R. (1975), “Omnibus test contours for departures from normality based on and b2”, Biometrika 62, 243–250. [3] Bowman, K.O., Shenton, L.R. (1986), Moment ( ) techniques. In: D’Agostino, R.B. and Stephens, M.A. (Editors). Goodness-of-Fit Techniques. Marcel Dekker, New York. [4] Chow, K.C., Watt, W.E. (1994), Practical use of the L-moments. In: Hipel, K.W. (Ed.). Stochastic and Statistical Methods in Hydrology and Environment Engineering, Kluwer Academic Publishers, Boston, Massachusetts, USA. [5] Chow, V.T. (1951), “A general formula for hydrologic frequency analysis”, Transaction of American Geophysics Union 32, 231–237. [6] Cox, D.R. (1968), “Notes on some aspects of regression analysis (with discussion)”, Journal of the Royal Statistical Society Series A 131, 265–279. [7] Cox, D.R., Small, N.J.H. (1978), “Testing multivariate normality”, Biometrika 65(2), 263–272. [8] D’Agostino, R.B., Pearson, E.S. (1973), “Tests for departure from normality. Empirical results for the distributions of b2 and ”, Biometrika, 60, 613–622. [9] D’Agostino, R.B., Stephens, M.A. (1986), Goodness-of-Fit Techniques. Marcel Dekker, New York. [10] Fisher, R. (1930), “The moments of the distribution for normal samples of measures of departures from normality”, Proceedings of the Royal Society A 130, 16–28. [11] Greenwood, J.A., Landwehr, J.M., Matalas, N.C., Wallis, J.R. (1979), “Probability weighted moments: Definition and relation to parameters of several distributions expressible in inverse form”, Water Resources Research 15, 1049–1054. [12] Haan, C.T. (2002), Statistical Methods in Hydrology. Ames, Iowa, Iowa State Press. [13] Heiberger, R.M., Holland, B. (2004), Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Science+Business Media, New York, U.S.A. [14] Hosking, J.R.M. (1990), “L-moments: analysis and estimation of distributions using linear combinations of order statistics”, Journal of the Royal Statistical Society Series B 52(1), 105–124. [15] Hosking, J.R.M., Wallis, J.R. (1993), “Some statistics useful in regional frequency analysis”, Water Resources Research 29(2), 271–281. [16] Hosking, J.R.M., Wallis, J.R. (1995), “A comparison of unbiased and plotting-position estimators of L-Moments”, Water Resources Research 31(8), 2019–2025. [17] Hosking, J.R.M., Wallis, J.R. (1997), Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge, U.K. [18] Kite, G.W. (1988), Frequency and Risk Analysis in Hydrology. Water Resources Publications. [19] Kottegoda, N.T. (1980), Stochastic water resources technology. Macmillan, London, U.K. [20] Mardia, K.V. (1970), “Measures of multivariate skewness and kurtosis with applications”, Biometrika 57(3), 519–530. [21] Mardia, K.V., Kent, J.T., Bibby, J.M. (1979), Multivariate Analysis. Academic Press, New York. [22] Vogel, R.M., Fennessey, N.M. (1993), “L moment diagrams should replace product moment diagrams”, Water Resources Research 29(6), 1745–1752. [23] Vogel, R.M., McMahon, T.A., Chiew, H.S. (1993), “Flood flow frequency model selection in Australia”, Journal of Hydrology 146, 421–449. [24] Vogel, R.M., Wilson I. (1996), “Probability distribution of annual maximum, mean, and minimum streamflows in the United States”, Journal of Hydrologic Engineering 1(2), 69–76. [25] Wallis, J.R. (1988), “Catastrophes, computing, and containment: living with our restless habitat”, Speculation in Science and Technology 11(4), 295–323. [26] Wu, Y.C. (2005), Establishing Confidence Interval for Goodness-of-Fit Test by Stochastic Simulation, Master Thesis, Department of Bioenvironmental Systems Engineering, National Taiwan University. Chapter 3 [1] Bellin, A., Rubin, Y. (1996), “HYDRO_GEN: A spatially distributed random field generator for correlated properties”, Stochastic Hydrology and Hydraulics 10, 253–278. [2] Cheng KS, Wei C, Cheng YB, Yeh HC. (2003), “Effect of spatial variation characteristics on contouring of design storm depth”, Hydrological Processes 17, 1755–1769. [3] Cheng, K.S., Hou, J.C., Liou, J.J. (2008), “Stochastic simulation of bivariate gamma distribution – A frequency-factor based approach”, Environmetrics. (in review) [4] Cressie, N. (1985), “Fitting variogram models by weighted least squares“, Mathematical Geology 17(5), 563–586. [5] Deutsch, C.V., Journel, A.G. (1992), GSLIB: Geostatistical Software Library and User's Guide. Oxford University Press, New York. [6] Franco, C., Soares, A., Delgado, J. (2006), “Geostatistical modelling of heavy metal contamination in the topsoil of Guadiamar river margins (S Spain) using a stochastic simulation technique”, Geoderma 136, 852–864. [7] Goovaerts, P. (1997), Geostatistics for Natural Resources Evaluation. Oxford University Press, New York. [8] Gotway, C.A. (1991), “Fitting semi-variogram models by weighted least squares”, Computers and Geosciences 17(1), 171–172. [9] Herrick, M.G., Benson, D.A., Meerschaert, M.M., McCall, K.R. (2002), “Hydraulic conductivity, velocity, and the order of the fractional dispersion derivative in a highly heterogeneous system”, Water Resources Research 38(11), 1227-1239. [10] Journel, A. (1974), “Geostatistics for conditional simulation of ore bodies”, Economic Geology 69, 673–687. [11] Journel, A.G., Huijbregts, C.J. (1978), Mining Geostatistics. Academic Press, London. [12] Kan, R. (2007), “From moments of sum to moments of products”, Journal of Multivariate Analysis, forthcoming. [13] Kendall, M.G., Stuart, A. (1977), The Advanced Theory of Statistics. Vol. 1 Distribution Theory, 4th edition, Charles Griffin, London. [14] Morrison, D.F. (1990), Multivariate Statistical Methods. 3rd edition, McGraw-Hill, New York. [15] National Research Council (2000), Risk Analysis and Uncertainty in Flood Damage Reduction Studies. National Academy Press. [16] Pardo-Iguzqúiza, E. (1999), “VARFIT: a fortran-77 program for fitting variogram models by weighted least squares”, Computers and Geosciences 25(3), 251–261. [17] Pardo-Iguzqúiza, E., Dowd, P.A. (2001), “VARIOG2D: a computer program for estimating the semi-variogram and its uncertainty”, Computers and Geosciences 27, 549–561. [18] Patel, J.K., Read, C.B. (1996), Handbook of the Normal Distribution. Marcel Dekker, New York. Chapter 4 [1] Cressie, N. (1985), “Fitting variogram models by weighted least squares“, Mathematical Geology 17(5), 563–586. [2] Cheng, K.S., Hou, J.C., Liou, J.J. (2008), “Stochastic simulation of bivariate gamma distribution – A frequency-factor based approach”, Environmetrics. (in review) [3] Deutsch, C. V., Journel A. G. (1993), GSLIB: Geostatistical Software Library and User’s Guide. Oxford university press, 2nd edition. [4] Elfeki, A., Dekking M. (2001), “A Markov Chain Model for Subsurface Characterization: Theory and Applications”, Mathematical Geology 33(5), 569–589. [5] Emery, X., (2007), “Using the Gibbs sampler for conditional simulation of Gaussian-based random fields”, Computers and Geosciences 33, 522–537. [6] Gaver D.P., Lewis, P.A.W. (1980), “First-order autoregressive gamma sequences and point processes”, Advanced in Applied Probability 12(3), 727-745. [7] Goodchild, M.F. (1980), “Algorithm 9: simulation of autocorrelation for aggregate data”, Environment and Planning A 12, 1073-1081. [8] Gotway, C.A. (1991), “Fitting semi-variogram models by weighted least squares”, Computers and Geosciences 17(1), 171–172. [9] Journel, A. G. (1980), “The lognormal approach to predicting local distributions of selective mining unit grades”, Mathematical Geology 12(4), 285-303. [10] Li, W., Zhang, C., Burt, J. E., Zhu A. X., Feyen, J. (2004), “Two-dimensional Markov Chain Simulation of Soil Type Spatial Distribution”, Soil Science Society of America Journal 68, 1479–1490. [11] Liou, J.J., Su, Y.F., Chiang, J.L., Cheng, K.S. (2008), “Gamma random field simulation by a Gaussian-gamma transformation method”. (in reviewed) [12] Mardia, K.V. (1988), “Multi-dimensional multivariate Gaussian Markov random fields with application to image processing”, Journal of Multivariate Analysis 24, 265–284. [13] Moran, P. (1969), “Statistical inference with bivariate gamma distributions”, Biometrika 56(3), 627-634. [14] Morrison, D.F. (1990), Multivariate Statistical Methods. New York: McGraw-Hill, 3rd edition. [15] Pardo-Iguzqúiza, E. (1999), “VARFIT: a fortran-77 program for fitting variogram models by weighted least squares”, Computers and Geosciences 25(3), 251–261. [16] Pardo-Iguzqúiza, E., Dowd, P.A. (2001), “VARIOG2D: a computer program for estimating the semi-variogram and its uncertainty”, Computers and Geosciences 27, 549–561. [17] Rivoirard, J. (1990), “A review of lognormal estimators for in situ reserves”, Mathematical Geology 22(2), 213-221. [18] Roth, C. (1998), “Is lognormal kriging suitable for local estimation?”, Mathematical Geology 30(8), 999-1009. [19] Rue, H., Held, L. (2005), Gaussian Markov Random Fields – Theory and Applications. Taylor & Francis. [20] Rue, H., Tjelmeland, H. (2002), “Fitting Gaussian Markov random fields to Gaussian fields”, Scandinavian Journal of Statistics 29, 31-49. [21] Schnabel, U., Tietje, O., Scholz, R. W. (2004), “Uncertainty assessment for management of soil contaminants with sparse data”, Environmental Management 33(6), 911–925. [22] Wolpert, R. L., Ickstadt, K. (1998), “Poission/gamma random field models for spatial statistics”, Biometrika 85(2), 251–267. [23] Yamamoto, J. K. (2007), “On unbiased backtransform of lognormal kriging estimates”, Computers and Geosciences 11, 219-234. [24] Zhang, J., Goodchild, M.F. (2002), Uncertainty in Geological Information. London; New York, Taylor & Francis. Chapter 5 [1] Cheng, K.S., Hueter, I., Hsu, E.C., Yeh, H.C. (2001), “A scale-invariant Gauss-Markov model for design storm hyetographs”, Journal of the American Water Resources Association 37(3), 723-736. [2] Liou, J.J., Wu, Y.C., Chiang, J.L., Cheng, K.S. (2007), “Assessing power of test for goodness-of-fit test using L-moment-ratios diagram”, Journal of Chinese Agricultural Engineering 53(4), 80-91. (in Chinese) | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/40384 | - |
| dc.description.abstract | 自然界許多現象之時間序列常使用隨機過程描述之，在應用層面上，亦須考慮隨機過程之空間效應，因此地理統計之隨機變域及其序率繁衍為重要研究課題。其不確定性分析為模式化環境隨機過程之重點工作，尤其於處理有限環境資訊時(不同時間、空間、尺度之觀測數量不足)，資料組體圖常為明顯偏態分布，不易決定隨機模型，具備高不確定性，此時序率模擬技術之選用更顯重要。
本論文針對過去應用序率模擬處理環境資訊之缺點，提出三種原創方法解決；首先是，「線性動差適合度檢定」，其提供線性動差比圖，考慮樣本長度之影響下，以一次繪圖比較許多候選分布之適配情況，改進傳統適合度檢定法之不足；其二為，「水文頻率因子之伽瑪隨機變域繁衍」，其提供非常態隨機變域另一解決之道，能繁衍明顯偏態分布之隨機變域；最後是，「方向性一階馬可夫鍊之非等向性指數半變異元模式隨機變域繁衍」，其有效率地繁衍隨機變域，且該隨機變域完全符合理論性質，非為近似結果。 本論文提出三種原創性方法，「線性動差適合度檢定」、「水文頻率因子之伽瑪隨機變域繁衍」與「方向性一階馬可夫鍊之非等向性指數半變異元模式隨機變域繁衍」，解決傳統環境計量序率模擬之缺點。三種方法均經過理論推導與試驗證實，為有效、原創、穩健之序率模擬工具，分述如下： 使用序率模擬法建立線性動差適合度檢定之接受域 水文變數最適機率分布之選定為水文頻率分析之首要項目。傳統上使用適合度檢定法進行該機率分布之選定，近年來，線性動差法被建議為水文頻率分析有效之工具，更利用線性動差比圖選定水文變數之最適機率分布。然而，過去研究中甚少提及樣本長度之不確定性影響。本研究利用序率模擬法以及兩種線性動差推估元，探討高斯與甘保分布樣本線性動差比值之特性，發現任意機率分布隨機變數之樣本線性動差比值呈現雙變數高斯分布，並據此基本假設建立高斯與甘保分布適合度檢定之接受域。該結果經驗證後符合設定之顯著水準，可應用於任意樣本長度之線性動差比值適合度檢定。建議之最小樣本長度為20筆。 使用高斯與伽瑪分布之轉換法進行伽瑪隨機變域之繁衍 隨機變域繁衍法可用於進行環境風險評估。最常使用之高斯循序繁衍法可用於繁衍高斯隨機變域。當環境變數為非高斯分布，利用等累積機率條件下，其經驗累積機率分布與高斯累積機率分布間之轉換關係，並配合高斯循序繁衍法進行其非高斯分布之隨機變域模擬工作。於此非高斯分布序率模擬過程中，需使用觀測資料計算其試驗半變異元與試驗累積機率曲線。然而，此依賴觀測資料始可進行之序率模擬，當缺乏觀測資訊時將無法進行，因此本研究提出伽瑪分布隨機變域模擬法，可在無觀測資訊下進行。其理論推演重要關鍵在於，伽瑪隨機變域與其相對應高斯隨機變域，兩者共變數函數間關係式之建立。經由數個伽瑪分布隨機變域之情境模擬計算，驗證得本研究所研提方法可適切繁衍出與設定相同之伽瑪隨機變域。 使用循序馬可夫鍊進行隨機變域之繁衍 隨機變域繁衍法可用於進行環境風險評估，而進行環境風險評估之精確性常受限於電腦運算時間。因此，前人研究中，利用馬可夫鍊數學上的精簡性與電腦運算上的速效性，發展許多隨機變域模擬法。例如使用馬可夫機率轉移矩陣發展雙一維馬可夫鍊隨機變域模擬與三個一維馬可夫鍊模擬隨機變域，或使用馬可夫鍊高斯隨機變域模擬近似高斯隨機變域。然而，上述繁衍法皆無法重現半變異元之固有特性，因此，本研究使用方向性一維馬可夫鍊進行隨機變域之模擬，由理論細部推演與數個隨機變域之情境模擬計算，驗證得本研究所研提方法可適切繁衍出與設定相同之隨機變域。能應用於非等向性半變異元隨機變域、常態分布或伽瑪隨機變域之繁衍。 | zh_TW |
| dc.description.abstract | Uncertainty analysis using stochastic simulation is an essential task in modeling environmental random processes. It is particularly important when the data under investigation is non-Gaussian, and available only at limited spatial or temporal points – the situation of having insufficient information in stochastic characteristics of environmental variables.
This dissertation presents three innovative stochastic simulation approaches to tackle uncertainties involved in three important topics in environmental modeling – (1) L-moment based goodness-of-fit (GOF) test, (2) gamma-random-field simulation, and (3) Markov chain simulation of random fields with anisotropic exponential variogram model. For the L-moment based GOF test, 95% acceptance region of the moment ratio diagram was established for both normal and Gumbel distributions. These acceptance regions are sample-size dependent, and, through stochastic simulation, empirical formulae for construction of 95% acceptance region with respect to arbitrary sample size between 20 and 1000 were established. For Gamma random field simulation, a theoretical relationship between the covariance functions of a gamma random field and its corresponding standard normal random field was derived. Then, through a gamma-to-Gaussian covariance matrix conversion, a sequential Gaussian random filed simulation was conducted using the required Gaussian covariance matrix. Finally, realizations of the gamma random field were generated by a Gaussian-gamma transformation. For random fields with exponential variogram model, a Markov chain simulation approach proposed in this study is shown to be more efficient and can be applied for anisotropic random field simulation. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-14T16:46:15Z (GMT). No. of bitstreams: 1 ntu-97-D92622005-1.pdf: 1588113 bytes, checksum: 04e4e7fbf2ca07492443335e09ffe071 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Contents
謝辭 i Abstract i 摘要 ii List of Tables vi List of Figures viii Chapter 1 Introduction 1 1.1 Stochastic simulation techniques and environmental random processes 1 1.2 Descriptions of main problems to be solved and objectives of this dissertation 4 1.3 Structures of this dissertation 5 References 5 Chapter 2 Establishing acceptance regions for L-moments-based goodness-of-fit tests by stochastic simulation 9 2.1 Introduction 9 2.2 The ordinary moment ratio diagram 11 2.2 L-moments and the L-moment ratio diagram 14 2.3 Stochastic simulation of normal and Gumbel distributions 19 2.4 Test for bivariate normality of sample L-skewness and L-kurtosis using the Mardia test 26 2.5 Establishing 95% acceptance regions for GOF test based on sample L-moments 31 2.6 Empirical relationships between parameters of acceptance region and sample size 37 2.7 Validity check of the LMRD acceptance regions 40 2.8 Conclusions 41 References 43 Chapter 3 Gamma random field simulation by a Gaussian-gamma transformation method 46 3.1 Introduction 46 3.2 Characterizing a random field 48 3.3 Conceptual description of a gamma random field simulation approach 49 3.4 Methodology and detailed procedures 52 3.4.1 Sequential Gaussian simulation 52 3.4.2 Covariance matrices conversion 57 3.4.3 Transforming Gaussian realizations to gamma realizations 61 3.5 Simulation and validation 62 3.6 Conclusions 66 References 75 Chapter 4 An innovative approach to sequential Markov chain random field simulation 78 4.1 Introduction 78 4.1.1 Applications and theoretical background of Markov chain random field simulation 78 4.1.2 Descriptions of section problems 79 4.1.3 Section objectives 80 4.2 Methodology 80 4.2.1 Markov properties in 1-D random field of exponential-semi- variogram model 80 4.2.2 Markov properties in 2-D random field of exponential-semi- variogram model 85 4.2.3 A new algorithm for Sequential Markov chain Random Field Simulation (SMRFS) 89 4.2.4 Simulation and validation 89 4.3 Results and discussions 92 4.3.1 An efficient algorithm for random field simulation based on theoretical derivations 92 4.3.2 Distribution-free Markov chain properties in spatial structure with exponential-semi-variogram 93 4.3.3 Validity results of the new algorithm for random field simulation 96 4.4 Conclusions 96 References 106 Chapter 5 Summary and future work 109 References 111 Appendix I. Proof of one-to-one mapping between Gamma and standard normal variables in Equation (3-16) 112 Appendix II. Proof of Equation (3-17) as a unique conversion 113 簡歷 114 | |
| dc.language.iso | en | |
| dc.subject | 適合度檢定 | zh_TW |
| dc.subject | 不確定性分析 | zh_TW |
| dc.subject | 環境隨機過程 | zh_TW |
| dc.subject | 線性動差法 | zh_TW |
| dc.subject | 伽瑪隨機變域 | zh_TW |
| dc.subject | 馬可夫鏈隨機變域 | zh_TW |
| dc.subject | goodness-of-fit test | en |
| dc.subject | uncertainty analysis | en |
| dc.subject | Markov chain random field | en |
| dc.subject | gamma random field | en |
| dc.subject | environmental random processes | en |
| dc.subject | linear-moment method | en |
| dc.title | 環境隨機過程之序率模擬 | zh_TW |
| dc.title | Stochastic Simulation of Environmental Random Processes | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 王如意(Ru-Yih Wang),游保杉(Pao-Shan Yu),黃文政(Wen-Cheng Huang),陳昶憲(Chang-Shian Chen) | |
| dc.subject.keyword | 不確定性分析,環境隨機過程,適合度檢定,線性動差法,伽瑪隨機變域,馬可夫鏈隨機變域, | zh_TW |
| dc.subject.keyword | uncertainty analysis,environmental random processes,goodness-of-fit test,linear-moment method,gamma random field,Markov chain random field, | en |
| dc.relation.page | 114 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-31 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-97-1.pdf 未授權公開取用 | 1.55 MB | Adobe PDF |
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