Parameter estimation

Related lectures (30)

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Explores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.

Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.

Explores parameter estimation of SDEs using Linear Response Theory and covers challenges, examples, algorithms, and convergence.

Covers the construction of a synthetic dataset for linear regression and the estimation of ground truth parameters.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Explores the application of linear algebra in data science, covering variance reduction, model distribution theory, and maximum likelihood estimates.

Covers linear regression, regularization, and probabilistic models in generating labels.

Covers linear regression for estimating train speed using least squares and regularization.

Covers parametric estimation in time series analysis, including integrated processes and seasonal modeling.