My solutions for the assignments and quizzes in the course Parameter and State Estimation, Fall 2020. Contents of the assignments:
- Statistics review: (conditional) expectations, variances, MAE prediction using Monte Carlo simulations
- Random signals, stationarity, Fisher Information: i. Check for stationarity: numerically and theoritically ii. Check for signal properties given process evolution model. Verify numerically (for given Signal to Noise ratio) iii. Building an ARIMA model for a given data iv. Fisher Information and Maximum Likelihood Estimator
- Power Spectral Decomposition & Fourier transform, MVUE, BLUE, CRLB, Hypothesis Testing, Efficiency of estimator using Monte Carlo simulations
- Distribution fitting, Maximum Likelihood estimator, Step-wise linear regression, RIDGE & LASSO (theoritical aspects), Bayesian Estimation using Jeffrey's Prior
- Recursive Least Squares, Pole Placement Observer, Kalman filter (for linear dynamical system: MATLAB, non linear: SIMULINK)