Hidden Markov Models for Time Series: An Introduction Using R, Second Edition

Walter Zucchini, Iain L. MacDonald, Roland Langrock

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition

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商品描述

Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses.

After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations.

The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations.

Features

  1. Presents an accessible overview of HMMs
  2. Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology
  3. Includes numerous theoretical and programming exercises
  4. Provides most of the analysed data sets online

New to the second edition

  1. A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process
  2. New case studies on animal movement, rainfall occurrence and capture–recapture data

商品描述(中文翻譯)

《隱藏馬可夫模型用於時間序列：使用R進行介紹，第二版》展示了隱藏馬可夫模型（HMMs）作為時間序列數據的通用模型的極大靈活性。本書提供了對模型及其應用的廣泛理解。

在介紹基本模型公式後，本書涵蓋了HMMs的估計、預測、解碼、預測、模型選擇和貝葉斯推斷。通過示例和應用，作者描述了如何擴展和推廣基本模型，使其可以應用於各種豐富的情況。

本書演示了如何將HMMs應用於各種類型的時間序列：連續值、循環、多變量、二進制、有界和無界計數以及分類觀察。它還討論了如何使用免費的計算環境R進行計算。

特點：

1. 提供了HMMs的易於理解的概述
2. 探索了在生態學、金融、流行病學、氣候學和社會學等各種應用
3. 包含了大量的理論和編程練習
4. 在線提供了大部分分析數據集

第二版的新內容：

1. 包括五個擴展章節，包括用於縱向數據的HMMs、隱藏半馬可夫模型和具有連續值狀態過程的模型
2. 新的案例研究，包括動物運動、降雨發生和捕獲-重新捕獲數據

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