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| Preface | |
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| Introduction, Coverage, Philosophy, and Computation | |
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| The Linear Model | |
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| Least-squares Estimation: Batch Processing | |
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| Least-squares Estimation: Singular-value Decomposition | |
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| Least-squares Estimation: Recursive Processing | |
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| Small-sample Properties of Estimators | |
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| Large-sample Properties of Estimators | |
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| Properties of Least-squares Estimators | |
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| Best Linear Unbiased Estimation | |
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| Likelihood | |
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| Maximum-likelihood Estimation | |
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| Multivariate Gaussian Random Variables | |
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| Mean-squared Estimation of Random Parameters | |
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| Maximum a Posteriori Estimation of Random Parameters | |
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| Elements of Discrete-time Gauss-Markov Random Sequences | |
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| State Estimation: Prediction | |
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| State Estimation: Filtering (the Kalman Filter) | |
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| State Estimation: Filtering Examples | |
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| State Estimation: Steady-state Kalman Filter and Its Relationship to a Digital Wiener Filter | |
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| State Estimation: Smoothing | |
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| State Estimation: Smoothing (General Results) | |
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| State Estimation for the Not-so-basic State-variable Model | |
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| Linearization and Discretization of Nonlinear Systems | |
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| Iterated Least Squares and Extended Kalman Filtering | |
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| Maximum-likelihood State and Parameter Estimation | |
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| Kalman-Bucy Filtering | |
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| A Sufficient Statistics and Statistical Estimation of Parameters | |
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| B Introduction to Higher-order Statistics | |
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| C Estimation and Applications of Higher-order Statistics | |
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| D Introduction to State-variable Models and Methods | |
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| App. A Glossary of Major Results | |
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| App. B Estimation Algorithm M-Files | |
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| App. C Answers to Summary Questions | |
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| References | |
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| Index | |