Statistical inference

Related lectures (31)

Page 3 of 4

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Discusses the steady-state Kalman predictor, provides examples, and compares Luenberger and Kalman filtering.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Explores methods for detecting and correcting parameter errors in power grids using statistical analysis and computational tools.

Explores exchangeability, statistical summaries for networks, invariance issues, and the Poisson Limit theorem in network statistics.

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.