Bayesian methods for modeling, identification and estimation of stochastic systems Bayesian Methods for Modeling, Identification andEstimation of Stochastic Systems
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Author
Bibliographic Information
- Title
-
Bayesian methods for modeling, identification and estimation of stochastic systems
- Other Title
-
Bayesian Methods for Modeling, Identification andEstimation of Stochastic Systems
- Author
-
姜, 興起
- Author(Another name)
-
キョウ, コウキ
- University
-
総合研究大学院大学
- Types of degree
-
博士 (学術)
- Grant ID
-
甲第39号
- Degree year
-
1993-03-23
Note and Description
博士論文
総研大甲第39号
Table of Contents
- ABSTRACT / p1 (0003.jp2)
- Contents / p3 (0005.jp2)
- 1 Preface / p1 (0007.jp2)
- 2 Nonstationary Regression Model / p5 (0011.jp2)
- 2.1 Introduction / p5 (0011.jp2)
- 2.2 Model and estimation procedure / p7 (0013.jp2)
- 2.3 A numerical example and some simulation studies / p13 (0019.jp2)
- 2.4 Applications to steel-GNP data analyses / p15 (0021.jp2)
- 2.5 Data transformation / p16 (0022.jp2)
- 3 Time Varying Coefficient Vector Autoregressive Model / p24 (0030.jp2)
- 3.1 Introduction / p24 (0030.jp2)
- 3.2 Stationary VAR modeling: a review / p27 (0033.jp2)
- 3.3 Time varying coefficient VAR modeling / p29 (0035.jp2)
- 3.4 Some applications of the model / p40 (0046.jp2)
- 3.5 An example:the application to earthquake data analysis / p42 (0048.jp2)
- 4 Identification and Estimation of Hyperparameters in Bayesian Linear Models / p57 (0063.jp2)
- 4.1 Introduction / p57 (0063.jp2)
- 4.2 Two kinds of expressions of the likelihood of hyperparameters and associated estimates / p61 (0067.jp2)
- 4.3 Equivalence of the two kinds of expressions of the likelihood / p65 (0071.jp2)
- 4.4 Choice of hyperparameters / p71 (0077.jp2)
- 4.5 Computational schemes / p73 (0079.jp2)
- 5 Summary and Concluding Remarks / p83 (0089.jp2)
- Acknowledgments / p86 (0092.jp2)
- References / p87 (0093.jp2)