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1.

図書
図書
1. Optimization and dynamical systems (: us ; : gw)
Uwe Helmke and John B. Moore ; with a foreword by R. Brockett
出版情報: London ; Tokyo : Springer-Verlag, c1994  xiv, 391 p. ; 25 cm
シリーズ名: Communications and control engineering
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2.

図書
図書
2. Linear optimal control
[by] Brian D.O. Anderson [and] John B. Moore
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1971  xiv, 399 p. ; 24 cm
シリーズ名: Prentice-Hall networks series
Prentice-Hall electrical engineering series
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3.

図書
図書
3. Optimal filtering
Brian D. O. Anderson, John B. Moore
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1979  x, 357 p. ; 24 cm
シリーズ名: Prentice-Hall information and system sciences series
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4.

図書
図書
4. Optimal control : linear quadratic methods
Brian D.O. Anderson, John B. Moore
出版情報: Englewood Cliffs, N.J. ; Tokyo : Prentice Hall, c1990  xi, 380 p. ; 25 cm
シリーズ名: Prentice-Hall information and system sciences series
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5.

図書
図書
5. Hidden Markov models : estimation and control (: us ; : gw)
Robert J. Elliott, Lakhdar Aggoun, John B. Moore
出版情報: New York ; Tokyo : Springer-Verlag, c1995  xii, 361 p. ; 25 cm
シリーズ名: Applications of mathematics ; 29
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目次情報: 続きを見る
Preface to the Second Edition
Preface
Introduction / Part I：
Hidden Markov Model Processing / 1：
Models, Objectives, and Methods / 1.1：
Book Outline / 1.2：
Discrete-Time HMM Estimation / Part II：
Discrete States and Discrete Observations / 2：
Model / 2.1：
Change of Measure / 2.3：
Unnormalized Estimates and Bayes' Formula / 2.4：
A General Unnormalized Recursive Filter / 2.5：
States, Transitions, and Occupation Times / 2.6：
Parameter Reestimation / 2.7：
Recursive Parameter Estimation / 2.8：
Quantized Observations / 2.9：
The Dependent Case / 2.10：
Problems and Notes / 2.11：
Continuous-Range Observations / 3：
State and Observation Processes / 3.1：
Conditional Expectations / 3.3：
Filter-Based State Estimation / 3.4：
Smoother-Based State Estimation / 3.6：
Vector Observations / 3.7：
HMMs with Colored Noise / 3.8：
Mixed-State HMM Estimation / 3.10：
Continuous-Range States and Observations / 3.11：
Linear Dynamics and Parameters / 4.1：
The ARMAX Model / 4.3：
Nonlinear Dynamics / 4.4：
Kalman Filter / 4.5：
State and Mode Estimation for Discrete-Time Jump Markov Systems / 4.6：
Example / 4.7：
A General Recursive Filter / 4.8：
Signal and Observations / 5.1：
Recursive Estimates / 5.3：
Extended Kalman Filter / 5.5：
Parameter Identification and Tracking / 5.6：
Formulation in Terms of Transition Densities / 5.7：
Dependent Case / 5.8：
Recursive Prediction Error Estimation / 5.9：
Practical Recursive Filters / 5.10：
Recursive Prediction Error HMM Algorithm / 6.1：
Example: Quadrature Amplitude Modulation / 6.3：
Example: Frequency Modulation / 6.4：
Coupled-Conditional Filters / 6.5：
Notes / 6.6：
Continuous-Time HMM Estimation / Part III：
Discrete-Range States and Observations / 7：
Dynamics / 7.1：
A General Finite-Dimensional Filter / 7.3：
Parameter Estimation / 7.4：
Markov Chains in Brownian Motion / 7.5：
The Model / 8.1：
Finite-Dimensional Predictors / 8.3：
A Non-Markov Finite-Dimensional Filter / 8.7：
Two-Dimensional HMM Estimation / 8.8：
Hidden Markov Random Fields / 9：
Discrete Signal and Observations / 9.1：
HMRF Observed in Gaussian Noise / 9.2：
Continuous-State HMRF / 9.3：
Example: A Mixed HMRF / 9.4：
HMM Optimal Control / 9.5：
Discrete-Time HMM Control / 10：
Control of Finite-State Processes / 10.1：
More General Processes / 10.2：
A Dependent Case / 10.3：
Risk-Sensitive Control of HMM / 10.4：
The Risk-Sensitive Control Problem / 11.1：
A Finite-Dimensional Example / 11.3：
Risk-Sensitive LQG Control / 11.6：
Continuous-Time HMM Control / 11.7：
Robust Control of a Partially Observed Markov Chain / 12.1：
Hybrid Conditionally Linear Process / 12.3：
Basic Probability Concepts / 12.5：
Continuous-Time Martingale Representation / B：
References
Author Index
Subject Index
Preface to the Second Edition
Preface
Introduction / Part I：
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