Information science Statistical signal processing using a stochastic model / Shun KATAOKA

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Information Science
Introduction to Contents
This is an introductory book on statistical signal processing techniques using a probability model, aimed at people who have studied fundamental calculus and signal processing.
Contents
☆ Because it is pre-publication information, it is explained as a bridge to statistical signal processing using a probability model.
Some changes may be made.
1. Signal Processing
1.1 Signal processing
1.2 Notation in this document
2. Signal and System
2.1 Definite and Irregular Signal
2.2 Time delay of signal
2.3 unit impulse signal
2.4 discrete time system
2.5 Impulse response
2.6 Causality of signal and system
2.7 Realization of system
2.8 Finite Impulse Response System
2.9 Infinite Impulse Response System
3.5 Summary Statistics and Least Squares Method
3.2 Histogram
3.3 Average and Variance
3.4 Relationship between Average and Variance and Histogram
3.6 Summary Statistics and Least Squares Method
4.1 Scatter and Correlation
4.2 Autocorrelation
4.3 Covariance and Correlation Coefficient
4.4 Relationship between Covariance and Correlation Coefficient and Scatter
4.5
Structure and Independence of Variables
Undirected and Directed Trees
Inference Tracing the Edges
Mate Probability Propagation Method
10. Linear Dynamic Systems
Excitation State-Space Model
Tairei Linear Dynamic Systems
Tairei Linear Dynamic Systems and Signal Processing Systems
Heave Linear Dynamic Systems
Probability Propagation Method
Neumann Filter
Neumann Smoothing
Neumann Maximum Likelihood Estimation in Linear Dynamic Models
Citations and References
Indexes 3.1 4.6 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 6.1 6.2 6.3 6.4 6.5 6.6 6.7 7.1 7.2 7.3 7.4 7.5 7.6 8.1 8.2 8.3 8.4 8.5 9.1 9.2 9.3 9.4 9.5 9.6 9.7 10.1 10.2 10.3 10.4 10.5 10.6 10.7