Lessons

Date	Recording	Title	Professor's notes
02 21	Recording	Introduction Stochastic processes (SP)	1.1_MIDA_Introduction 1.2_MIDA_Stochastic_Processes
02 22	Recording	Weak description of SP Stationary stochastic processes (SSP) White noise (WN) Moving average processes (MA)	1.2_MIDA_Stochastic_Processes 1.3_MIDA_Model_Classes
02 23	Recording	MA stationary? Covariance properties for SSP MA(inf) processes MA(inf) stationary?	1.3_MIDA_Model_Classes
02 28	Recording	Auto Regressive AR ARMA Steady state solutions Shift Operator Operational representation of ARMA Transfer function	1.3_MIDA_Model_Classes
03 01	Recording	Transfer function composition (series/parallel) Switch shift operator powers Zeros and poles Assintotically stable Minimum fase When ARMA is well-defined?	1.3_MIDA_Model_Classes
03 02	Recording	Solutions different from steady-state Computing ARMA weak (wide-sense) characterization	1.3_MIDA_Model_Classes
03 08	Recording	Non zero mean ARMA Gain theorem Unbiased processes ARMAX Frequency domain Properties of spectrum Spectrum of digital filter output	1.3_MIDA_Model_Classes 1.4_MIDA_Frequency_Domain_Analysis
03 09	Recording	Spectrum antitrasformation Relation between covariance and spectrum Wiener-Kinchin theorem Spectrum of ARMA 4 sources of uniqueness of ARMA	1.4_MIDA_Frequency_Domain_Analysis
03 10	Recording	4th source of uniqueness of ARMA Canonical representation of ARMA Introduction to linear optimal prediction Mean square error	1.4_MIDA_Frequency_Domain_Analysis 1.5_MIDA_Prediction
03 15	Recording	Optimal linear predictor from noise Long k-step division Optimal linear predictor from output
03 16	Recording	Reconstructing WN from output values Predictors from finite sequence of values Optimal prediction of non-sero mean ARMA
03 22	Recording	ARMAX predictors Model identification introduction Black box and grey box model identification PEM identification
03 23	Recording	PEM identification cost function PEM cost function computation Least square identification