Topics covered in this course: Statistical analysis and optimization of data
- Basic concepts, expectation values, variance, covariance, correlation functions and errors
- Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions
- Central elements of Bayesian statistics and modeling
- Gradient methods for data optimization
- Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm
- Linear methods for regression and classification
- Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods
- Practical optimization using Singular-value decomposition and least squares for parameterizing data