Handbook of Mixture Analysis
暫譯: 混合分析手冊
Fruhwirth-Schnatter, Sylvia, Celeux, Gilles, Robert, Christian P.
- 出版商: CRC
- 出版日期: 2020-12-18
- 售價: $2,930
- 貴賓價: 9.5 折 $2,784
- 語言: 英文
- 頁數: 498
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367732068
- ISBN-13: 9780367732066
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相關主題
商品描述
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time.
The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.
Features:
- Provides a comprehensive overview of the methods and applications of mixture modelling and analysis
- Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications
- Contains many worked examples using real data, together with computational implementation, to illustrate the methods described
- Includes contributions from the leading researchers in the field
The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
商品描述(中文翻譯)
混合模型已經存在超過150年，並且在許多統計建模的領域中被廣泛應用，作為一種多功能且多面向的工具。它們可以應用於各種數據：單變量或多變量、連續或類別、橫斷面、時間序列、網絡等。混合分析是統計學和機器學習中一個非常活躍的研究主題，方法論和應用方面不斷有新的發展。
《混合分析手冊》是一部非常及時的出版物，提供了這一重要研究領域方法和應用的廣泛概述。它涵蓋了多個主題，包括EM算法、貝葉斯混合模型、基於模型的聚類、高維數據、隱馬可夫模型，以及在金融、基因組學和天文學中的應用。
特點：
- 提供混合建模和分析方法及應用的全面概述
- 分為三個部分：基礎與方法；混合建模及擴展；以及選定的應用
- 包含許多使用真實數據的實例，並提供計算實現，以說明所描述的方法
- 包括該領域領先研究者的貢獻
《混合分析手冊》針對的是研究生和剛進入該領域的年輕研究者。對於任何在此領域工作的人來說，無論是開發新方法論，還是將模型應用於真實科學問題，它都將是一個重要的參考資料。
作者簡介
Sylvia Frühwirth-Schnatter is Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics, Vienna University of Economics and Business, Austria. She has contributed to research in Bayesian modelling and MCMC inference for a broad range of models, including finite mixture and Markov switching models as well as state space models. She is particularly interested in applications of Bayesian inference in economics, finance, and business. She started to work on finite mixture and Markov switching models 20 years ago and has published more than 20 articles in this area in leading journals such as JASA, JCGS, and Journal of Applied Econometrics. Her monograph Finite Mixture and Markov Switching Models (2006) was awarded the Morris-DeGroot Price 2007 by ISBA. In 2014, she was elected Member of the Austrian Academy of Sciences.
Gilles Celeux is Director of research emeritus with INRIA Saclay-Île-de-France, France. He has conducted research in statistical learning, model-based clustering and model selection for more than 35 years and he leaded to Inria teams. His first paper on mixture modelling was written in 1981 and he is one of the co-organisators of the summer working group on model-based clustering since 1994. He has published more than 40 papers in international Journals of Statistics and wrote two textbooks in French on Classification. He was Editor-in-Chief of Statistics and Computing between 2006 and 2012 and he is the present Editor-in-Chief of the Journal of the French Statistical Society since 2012.
Christian P. Robert is Professor of Mathematics at CEREMADE, Université Paris-Dauphine, PSL Research University, France, and Professor of Statistics at the Department of Statistics, University of Warwick, UK. He has conducted research in Bayesian inference and computational methods covering Monte Carlo, MCMC, and ABC techniques, for more than 30 years, writing The Bayesian Choice (2001) and Monte Carlo Statistical Methods (2004) with George Casella. His first paper on mixture modelling was written in 1989 on radiograph image modelling. His fruitful collaboration with Mike Titterington on this topic spans two enjoyable decades of visits to Glasgow, Scotland. He has organised three conferences on the subject of mixture inference, with the last one at ICMS leading to the edited book Mixtures: Estimation and Applications (2011), co-authored with K. L. Mengersen and D. M. Titterington.
作者簡介(中文翻譯)
Sylvia Frühwirth-Schnatter 是奧地利維也納經濟與商業大學財務、會計與統計系的應用統計與計量經濟學教授。她在貝葉斯建模和馬可夫鏈蒙地卡羅（MCMC）推斷方面的研究涵蓋了廣泛的模型，包括有限混合模型和馬可夫切換模型，以及狀態空間模型。她特別關注貝葉斯推斷在經濟學、金融和商業中的應用。她在20年前開始研究有限混合模型和馬可夫切換模型，並在《JASA》、《JCGS》和《應用計量經濟學期刊》等領先期刊上發表了20多篇相關文章。她的專著《Finite Mixture and Markov Switching Models》（2006）於2007年獲得ISBA頒發的Morris-DeGroot獎。2014年，她被選為奧地利科學院會員。
Gilles Celeux 是法國INRIA Saclay-Île-de-France的榮譽研究主任。他在統計學習、基於模型的聚類和模型選擇方面進行了超過35年的研究，並領導了Inria團隊。他在1981年撰寫了關於混合建模的第一篇論文，自1994年以來，他是基於模型的聚類夏季工作組的共同組織者之一。他在國際統計期刊上發表了40多篇論文，並用法語撰寫了兩本有關分類的教科書。他在2006年至2012年間擔任《Statistics and Computing》的主編，並自2012年以來擔任《法國統計學會期刊》的現任主編。
Christian P. Robert 是法國巴黎多芬大學CEREMADE的數學教授，以及英國華威大學統計系的統計學教授。他在貝葉斯推斷和計算方法方面進行了超過30年的研究，涵蓋了蒙地卡羅、MCMC和ABC技術，並與George Casella合著了《The Bayesian Choice》（2001）和《Monte Carlo Statistical Methods》（2004）。他在1989年撰寫了關於放射影像建模的第一篇混合建模論文。他與Mike Titterington在這一主題上的富有成效的合作跨越了兩個愉快的十年，期間多次造訪蘇格蘭格拉斯哥。他組織了三次有關混合推斷的會議，最近的一次在ICMS舉行，並編輯了與K. L. Mengersen和D. M. Titterington共同撰寫的書籍《Mixtures: Estimation and Applications》（2011）。
