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Concept

Bayesian information criterion

Related lectures (23)

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Model Choice and Prediction

Explores model choice, prediction, and forecasting techniques in time series analysis.

The Information Criteria of Takeuchi and Akaike

Covers the estimation of optimism for predictors via statistical models and the application of AIC to linear regression.

Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Self-supervised learning

Explores self-supervised learning, embedding techniques, HSIC, and geometric information in downstream tasks.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Model Selection: Least Squares

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Central Limit Theorem: Proof

Presents the proof of the Central Limit Theorem using Lindeberg's principle.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Equivalence Criterion: Proof and Examples

Covers the equivalence criterion for series convergence and provides illustrative examples.

Estimation and Forecasting in Time Series

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

LASSO Regression: Sparse Signal Induction

Explores LASSO regression for inducing sparsity in signals through gradient descent.

Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Variable Selection Methods Log in to Mediaspace to watch this video

Explores variable selection methods and the bias-variance trade-off in statistical modeling.

Model Selection in Time Series Analysis Log in to Mediaspace to watch this video

Covers model selection, diagnostics, and forecasting in time series analysis, emphasizing the challenges of determining the model order based on autocorrelation and partial autocorrelation functions.

Binary Response: Link Functions Log in to Mediaspace to watch this video

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Data Representations & Processing Log in to Mediaspace to watch this video

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Untitled Log in to Mediaspace to watch this video

Data Representations and Processing Log in to Mediaspace to watch this video

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Model Checking and Residuals Log in to Mediaspace to watch this video

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.