Handbook of Mixed Membership Models and Their Applications
暫譯: 混合成員模型及其應用手冊
Airoldi, Edoardo M., Blei, David, Erosheva, Elena A.
- 出版商: CRC
- 出版日期: 2021-03-31
- 售價: $3,560
- 貴賓價: 9.5 折 $3,382
- 語言: 英文
- 頁數: 618
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367330849
- ISBN-13: 9780367330842
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相關主題
商品描述
In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology.
Through examples using real data sets, you'll discover how to characterize complex multivariate data in:
- Studies involving genetic databases
- Patterns in the progression of diseases and disabilities
- Combinations of topics covered by text documents
- Political ideology or electorate voting patterns
- Heterogeneous relationships in networks, and much more
The handbook spans more than 20 years of the editors' and contributors' statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.
商品描述(中文翻譯)
為了滿足科學界對於更具多樣性和結構化的統計數據解釋的需求，研究人員已經發現如何將個別數據點建模為屬於多個群體。混合成員模型及其應用手冊將向您展示如何使用這些靈活的建模工具來揭示現代高維多變量數據中的隱藏模式。它探討了這些模型在各種應用場景中的使用，包括調查數據、人口遺傳學、文本分析、圖像處理與註釋以及分子生物學。
通過使用真實數據集的範例，您將發現如何在以下方面描述複雜的多變量數據：
- 涉及基因數據庫的研究
- 疾病和殘疾進展的模式
- 文本文件所涵蓋主題的組合
- 政治意識形態或選民投票模式
- 網絡中的異質關係，以及更多
本手冊涵蓋了編輯和貢獻者在該領域超過20年的統計工作。頂尖研究人員比較了部分和混合成員模型，解釋了如何解釋混合成員，深入探討因子分析，並描述了非參數混合成員模型。他們還介紹了混合成員模型在文本分析、序列和排名數據以及網絡數據中的擴展，以及半監督混合成員模型。
作者簡介
Edoardo M. Airoldi is an associate professor of statistics at Harvard University. Dr. Airoldi's current research focuses on statistical theory and methods for designing and analyzing experiments in the presence of network interference as well as on modeling and inferential issues when dealing with network data.
David M. Blei is a professor of statistics and computer science at Columbia University. Dr. Blei's research is in statistical machine learning involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference.
Elena A. Erosheva is an associate professor of statistics and social work at the University of Washington, where she is a core member of the Center for Statistics and the Social Sciences. Dr. Erosheva's research focuses on the development and application of modern statistical methods to address important issues in the social, medical, and health sciences.
Stephen E. Fienberg is the Maurice Falk University Professor of Statistics and Social Science at Carnegie Mellon University, where he is co-director of the Living Analytics Research Centre and a member of the Department of Statistics, the Machine Learning Department, the Heinz College, and Cylab. Dr. Fienberg's research includes the development of statistical methods for categorical data analysis and network data analysis.
作者簡介(中文翻譯)
Edoardo M. Airoldi 是哈佛大學的統計學副教授。Airoldi 博士目前的研究重點是統計理論和方法，專注於在網絡干擾存在的情況下設計和分析實驗，以及在處理網絡數據時的建模和推斷問題。
David M. Blei 是哥倫比亞大學的統計學和計算機科學教授。Blei 博士的研究領域是統計機器學習，涉及概率主題模型、貝葉斯非參數方法和近似後驗推斷。
Elena A. Erosheva 是華盛頓大學的統計學和社會工作副教授，她是統計與社會科學中心的核心成員。Erosheva 博士的研究專注於現代統計方法的開發和應用，以解決社會、醫療和健康科學中的重要問題。
Stephen E. Fienberg 是卡內基梅隆大學的莫里斯·福克大學統計學和社會科學教授，他是生活分析研究中心的共同主任，也是統計系、機器學習系、海因茨學院和Cylab的成員。Fienberg 博士的研究包括為類別數據分析和網絡數據分析開發統計方法。
