隨機過程 Stochastic Processes--陳伯寧 / 交大

source: NCTU OCW 2017年9月20日
This course intends to provide students with the necessary (fundamental and advanced) background on random processes.
授課教師：電機工程學系 陳伯寧老師
課程資訊：http://ocw.nctu.edu.tw/course_detail....
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

1 8:29 Lec 00 隨機過程課程介紹
2 37:18 Lec 9-1 General Concepts Definitions (1/7)
3 52:00 Lec 9-1 General Concepts Definitions (2/7)
4 51:39 Lec 9-1 General Concepts Definitions (3/7)
5 47:03 Lec 9-1 General Concepts Definitions (4/7)
6 46:42 Lec 9-1 General Concepts Definitions (5/7)
7 43:23 Lec 9-1 General Concepts Definitions (6/7)
8 51:32 Lec 9-1 General Concepts Definitions (7/7)
9 44:58 Lec 9-2 General Concepts Systems with Stochastic Inputs (1/3)
10 52:11 Lec 9-2 General Concepts Systems with Stochastic Inputs (2/3)
11 23:40 Lec 9-2 General Concepts Systems with Stochastic Inputs (3/3)
12 1:03:34 Lec 9-3 General Concepts The Power Spectrum (1/3)
13 48:57 Lec 9-3 General Concepts The Power Spectrum (2/3)
14 21:27 Lec 9-3 General Concepts The Power Spectrum (3/3)
15 1:01:35 Lec 9-4 General Concepts Discrete-Time Processes
16 57:56 Lec 10-3 Random Walks and Other Applications Modulation (1/3)
17 56:41 Lec 10-3 Random Walks and Other Applications Modulation (2/3)
18 42:31 Lec 10-3 Random Walks and Other Applications Modulation (3/3)
19 33:14 Lec 10-4 Random Walks and Other Applications Cyclostationary Processes
20 1:11:14 Lec 10-5 Random Walks and Other Applications Bandlimited Processes and Sampling Theory
21 1:08:22 Lec 10-6 Deterministic Signals in Noise & Appendix 10A The Poisson Sum Formula
22 1:16:16 Lec 11-1 Spectral Representation Factorization and Innovations
23 47:31 Lec 11-2 Spectral Representation Finite-Order Systems and State Variables (1/3)
24 42:49 Lec 11-2 Spectral Representation Finite-Order Systems and State Variables (2/3)
25 51:03 Lec 11-2 Spectral Representation Finite-Order Systems and State Variables (3/3)
26 25:20 Lec 11-3 Spectral Representation Fourier Series and Karhunen-Lo`eve Expansions (½)
27 1:16:33 Lec 11-3 Spectral Representation Fourier Series and Karhunen-Lo`eve Expansions (2/2)
28 55:08 Lec 11-4 Spectral Representation Spectral Representation of Random Processes (½)
29 39:55 Lec 11-4 Spectral Representation Spectral Representation of Random Processes (2/2)
30 53:24 Lec 12-0 Spectrum Estimation Ergodicity based on Shift-invariant Event
31 40:26 Lec 12-1 Spectrum Estimation Ergodicity (1/3)
32 17:04 Lec 12-1 Spectrum Estimation Ergodicity (2/3)
33 58:52 Lec 12-1 Spectrum Estimation Ergodicity (3/3)
34 57:01 Lec 12-2 Spectrum Estimation (1/3)
35 37:47 Lec 12-2 Spectrum Estimation (2/3)
36 51:57 Lec 12-2 Spectrum Estimation (3/3)
37 37:44 Lec 13-1 Mean Square Estimation Introduction (1/2)
38 29:47 Lec 13-1 Mean Square Estimation Introduction (2/2)
39 1:03:21 Lec 13-2 Mean Square Estimation Prediction (½)
40 54:46 Lec 13-2 Mean Square Estimation Prediction (2/2)