Hidden Markov models : estimation and control

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Hidden Markov models : estimation and control / Robert J. Elliott, Lakhdar Aggoun, John B. Moore

資料種別:

図書

出版情報:

New York ; Tokyo : Springer-Verlag, c1995

形態:

xii, 361 p. ; 25 cm

シリーズ名:

Applications of mathematics ; 29 <BA00256802>

著者名:

ISBN:

9780387943640 [0387943641] (: us)
9783540943648 [3540943641] (: gw)

書誌ID:

BA24038362

子書誌情報

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書誌詳細

注記:

Bibliography: p. [343]-353
Includes indexes

主題:

Markov processes

言語:

英語

目次情報:

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：

Hidden Markov Model Processing / 1：

Models, Objectives, and Methods / 1.1：

Book Outline / 1.2：

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