Bayesian Time Series Models (Hardcover)

David Barber, A. Taylan Cemgil, Silvia Chiappa

  • 出版商: Cambridge
  • 出版日期: 2011-08-11
  • 售價: $4,980
  • 貴賓價: 9.5$4,731
  • 語言: 英文
  • 頁數: 432
  • 裝訂: Hardcover
  • ISBN: 0521196760
  • ISBN-13: 9780521196765
  • 相關分類: 機率統計學 Probability-and-statistics
  • 海外代購書籍(需單獨結帳)

買這商品的人也買了...

商品描述

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.

商品描述(中文翻譯)

「接下來會發生什麼事?」時間序列資料揭示了答案,而貝葉斯方法代表了學習它們所要傳達的最前沿技術。這本雄心勃勃的書是對貝葉斯時間序列技術新興知識庫的首次統一論述。利用概率圖模型的統一框架,本書涵蓋了蒙特卡羅和確定性的近似方案,並介紹了在各種應用環境中的切換、多對象、非參數和基於代理的模型。它展示了基本框架支持快速創建針對特定應用的模型,並揭示了其實施的計算複雜性。作者涵蓋了傳統學科,如統計學和工程學,以及機器學習和模式識別等最近建立的領域。本書引導具有應用概率基礎知識但沒有時間序列分析經驗的讀者,從基本概念到研究和實踐的最新成果。