Bayesian Parameter Estimation
Lecture 1:
Dick Furnstahl, 2019-06-10
- Overview of parameter estimation, frequentist vs. Bayesian
- Examples: parameters of Gaussian noise, fitting a straight line
- Parameter estimation: fitting a straight line [ipynb]
Lecture 2:
Dick Furnstahl, 2019-06-11
- Central limit theorem
- Correlations and the likelihood / posterior
- Amplitude of a signal in the presence of background
- Signal and background [ipynb]
Lecture 3:
Dick Furnstahl, 2019-06-13
- Recap of signal + background
- Error probagation for multivariate Gaussians
- Maximum likelihood in linear algebra form
- Data analysis recipes: Fitting a model to data — article from Hogg, Bovy, and Lang about real-world fitting (many interesting annotations!).
Lecture 4:
Dick Furnstahl, 2019-06-25
- Sloppy models
- SVD and PCA
- Lecture material and afternoon exercises [ipynb]
- A Singularly Valuable Decomposition: The SVD of a Matrix (Kalman) [pdf]