請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94105完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊鈞澔 | zh_TW |
| dc.contributor.advisor | Chun-Hao Yang | en |
| dc.contributor.author | 劉宸熙 | zh_TW |
| dc.contributor.author | Chen-Hsi Liu | en |
| dc.date.accessioned | 2024-08-14T16:43:00Z | - |
| dc.date.available | 2024-08-15 | - |
| dc.date.copyright | 2024-08-14 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-02 | - |
| dc.identifier.citation | Asur, S., Parthasarathy, S., and Ucar, D. (2007). An eventbased framework for characterizing the evolutionary behavior of interaction graphs. In Proceedings of the 13th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’07, page 913–921, New York, NY, USA. Association for Computing Machinery.
AtayKayis, A. and Massam, H. (2005). A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models. Biometrika, 92(2):317–335. Barbieri, M. M. and Berger, J. O. (2004). Optimal predictive model selection. The Annals of Statistics, 32(3):870–897. Cao, X., Khare, K., and Ghosh, M. (2016). Posterior graph selection and estimation consistency for highdimensional Bayesian DAG models. The Annals of Statistics, 47:319–348. Castelletti, F., Consonni, G., Vedova, M. L. D., and Peluso, S. (2018). Learning Markov equivalence classes of directed acyclic graphs: An objective Bayes approach. Bayesian Analysis, 13(4):1235–1260. Consonni, G., Rocca, L. L., and Peluso, S. (2017). Objective Bayes covariate-adjusted sparse graphical model selection. Scandinavian Journal of Statistics, 44(3):741–764. Dawid, A. P. and Lauritzen, S. L. (1993). Hyper Markov laws in the statistical analysis of decomposable graphical models. The Annals of Statistics, 21(3):1272–1317. Fortunato, S. and Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659:1–44. Friedman, J., Hastie, T., and Tibshirani, R. (2007). Sparse inverse covariance estimation with the graphical LASSO. Biostatistics, 9(3):432–441. Girvan, M. and Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821–7826. Giudici, P. and Green, P. (1999). Decomposable graphical Gaussian model determination. Biometrika, 86(4):785–801. Giudici, P. and Spelta, A. (2016). Graphical network models for international financial flows. Journal of Business & Economic Statistics, 34(1):128–138. Goodreau, S. M. (2007). Advances in exponential random graph (p*) models applied to a large social network. Social Networks, 29(2):231–248. Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711–732. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N. (2006). Learning systems of concepts with an infinite relational model. In Proceedings of the 21st National Conference on Artificial Intelligence Volume 1, AAAI’06, page 381–388. AAAI Press. Kundu, S., Mallick, B. K., and Baladandayuthapani, V. (2019). Efficient Bayesian regularization for graphical model selection. Bayesian Analysis, 14(2):449–476. Lauritzen, S. L. (1996). Graphical models. Oxford University Press. Lenkoski, A. and Dobra, A. (2011). Computational aspects related to inference in Gaussian graphical models with the G-Wishart prior. Journal of Computational and Graphical Statistics, 20(1):140–157. Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4):395–416. Mohammadi, A. and Wit, E. C. (2015). Bayesian structure learning in sparse Gaussian graphical models. Bayesian Analysis, 10(1):109–138. Murray, I., Ghahramani, Z., and MacKay, D. J. C. (2006). MCMC for doublyintractable distributions. In Proceedings of the TwentySecond Conference on Uncertainty in Artificial Intelligence, UAI’06, page 359–366, Arlington, Virginia, USA. AUAI Press. Mørup, M. and Schmidt, M. (2012). Bayesian community detection. Neural computation, 24:2434–2456. Newman, M. E. J. and Girvan, M. (2004). Finding and evaluating community structure in networks. Phys. Rev. E, 69:026113. Nowicki, K. and Snijders, T. A. B. (2001). Estimation and prediction for stochastic block structures. Journal of the American Statistical Association, 96(455):1077–1087. Reza Mohammadi, H. M. and Letac, G. (2023). Accelerating Bayesian structure learning in sparse Gaussian graphical models. Journal of the American Statistical Association, 118(542):1345–1358. Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., and Nolan, G. P. (2005). Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523–529. Sedgewick, A. J., Buschur, K., Shi, I., Ramsey, J. D., Raghu, V. K., Manatakis, D. V., Zhang, Y., Bon, J., Chandra, D., Karoleski, C., Sciurba, F. C., Spirtes, P., Glymour, C., and Benos, P. V. (2018). Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinformatics, 35(7):1204–1212. Shutta, K. H., De Vito, R., Scholtens, D. M., and Balasubramanian, R. (2022). Gaussian graphical models with applications to omics analyses. Statistics in Medicine, 41(25):5150–5187. Speed, T. P. and Kiiveri, H. T. (1986). Gaussian Markov distributions over finite graphs. The Annals of Statistics, 14(1):138–150. Sun, S., Zhu, Y., and Xu, J. (2014). Adaptive variable clustering in Gaussian graphical models. In Kaski, S. and Corander, J., editors, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, volume 33, pages 931–939, Reykjavik, Iceland. PMLR. Tan, K. M., Witten, D., and Shojaie, A. (2015). The cluster graphical LASSO for improved estimation of Gaussian graphical models. Computational Statistics and Data Analysis, 85:23–36. Uhler, C., Lenkoski, A., and Richards, D. (2018). Exact formulas for the normalizing constants of Wishart distributions for graphical models. The Annals of Statistics, 46(1):90–118. Wang, H. (2012). Bayesian graphical LASSO models and efficient posterior computation. Bayesian Analysis, 7(4):867–886. Wang, H. and Li, S. Z. (2012). Efficient Gaussian graphical model determination under G-Wishart prior distributions. Electronic Journal of Statistics, 6:168–198. Zhang, L. and Ji, Q. (2010). Image segmentation with a unified graphical model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8):1406–1425. Zhou, H. (2003). Network landscape from a Brownian particle’s perspective. Phys. Rev. E, 67:041908. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94105 | - |
| dc.description.abstract | 圖或者說網絡在社群偵測和圖模型中分別擔任輸入和輸出的角色。由於理解社群結構可提高對圖結構的理解，因此在使用圖模型獲得圖結構之估計後，人們渴望識別潛在的分組。不同於先使用圖模型再對其估計值進行社群偵測，我們的層次圖模型同時估計圖結構和社群結構。該模型將常態-威夏特模型的部分特徵與貝氏社群偵測相融合。最後，我們為後驗推斷開發了一種高效的吉布斯取樣。 | zh_TW |
| dc.description.abstract | Graphs or networks respectively serve as input and output in community detection and graphical models. As understanding community structure enriches our comprehension of graphs, there is a desire to identify potential groupings after obtaining a graph estimate using a graphical model. Rather than sequentially applying a graphical model followed by community structure detection, our hierarchical graphical model concurrently estimates both the graph and community structures. This model blends aspects of the normal-Wishart model with Bayesian community detection. Finally, we develop an efficient Gibbs sampler for posterior inference. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-14T16:43:00Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-14T16:43:00Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Contents
Page Acknowledgements i 摘要 iii Abstract v Contents vii List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Gaussian Graphical Model 1 1.2 Community Detection in Graphs 2 1.3 Motivation and Methodology 4 Chapter 2 Preliminaries 7 2.1 Graph 7 2.2 Graphical Model 8 2.3 SBM and IRM 12 Chapter 3 Graphical community detection 15 3.1 Graphical Community Detection model 15 3.2 Gibbs Sampler 18 3.3 Approximation for normalizing constant 21 Chapter 4 Simulations and real data analysis 25 4.1 Simulation result 25 4.2 TCGA ovarian cancer 30 Chapter 5 Discussion 33 5.1 Discussion 33 References 35 | - |
| dc.language.iso | en | - |
| dc.title | 利用層次圖模型進行社群偵測及圖結構之估計 | zh_TW |
| dc.title | A Unified Framework for Graph Estimation and Community Detection using Hierarchical Graphical Models | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳裕庭;張升懋 | zh_TW |
| dc.contributor.oralexamcommittee | Yu-Ting Chen;Sheng-Mao Chang | en |
| dc.subject.keyword | 社群偵測,圖模型,共變異數選擇,貝氏推論,無限關係模型, | zh_TW |
| dc.subject.keyword | Community detection,Graphical model,Covariance selection,Bayesian inference,Infinite relationship model, | en |
| dc.relation.page | 39 | - |
| dc.identifier.doi | 10.6342/NTU202401933 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-06 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 統計與數據科學研究所 | - |
| 顯示於系所單位： | 統計與數據科學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf | 909.12 kB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。