Bayesian Linear Regression

Overview

• Introduction to Bayesian Linear Regression
• Discussion on the requests for a statistical model using Bayesian statistics
• Connection to Ordinary Least Squares (OLS) and regularization methods (LASSO and Ridge)

Linear Regression Framework

Key Concepts

• Linear model with known data matrix X (n by p)

• Response variable Y generated by a linear combination of features:

  $$ Y = X \beta + \epsilon $$

• Goal: solve for beta in the linear model.

Assumptions

1. Errors ε are normally distributed with mean 0 and variance σ².
2. Each Y_i is normally distributed with mean β^T X_i and variance σ².

Bias-Variance Tradeoff

• OLS solution has high variance
• Small changes in X can lead to large changes in the beta estimates.
• Regularization methods (LASSO and Ridge) help mitigate this issue by adding penalty terms.

Regularization

LASSO and Ridge

• LASSO: Uses L1 norm to promote sparsity in beta estimates.
• Ridge: Uses L2 norm to shrink the values of beta.

Optimization Goals

1. Minimize errors from predictions (fit to data).
2. Keep betas small (regularization).

Bayesian Framework

Posterior Probability

• Focus on probability of beta given y.
• Aim to maximize the posterior probability (MAP estimate).

Bayes' Theorem

• Posterior can be expressed as:

  $$ P(\beta | Y) \propto P(Y | \beta) \cdot P(\beta) $$

• Y is the known data, β is the unknown parameter vector.

• Simplifying the posterior by focusing on terms dependent on beta.

Likelihood and Prior

• Likelihood: Probability of observing data given beta.
• Prior: Probability of observing beta before any data is seen.

Choosing a Prior

• The choice of prior impacts the outcome and needs careful consideration.
• Example of a prior: assume 𝛽_j ~ Normal(0, τ²) to emphasize that small beta values are likely.

Connection Between Bayesian and Regularization Methods

• Regularization effectively keeps beta estimates small, mirroring Bayesian prior influence.
• The optimization problem derived from Bayesian statistics can yield equivalent results as regularization (e.g., Ridge corresponds to Gaussian prior; LASSO corresponds to Laplacian prior).

Final Thoughts

• Bayesian approach is a different perspective rather than inherently "better".
• Questions about the choice of prior and its implications remain valid in Bayesian statistics.

Conclusion

• Summary of Bayesian linear regression as a powerful lens to approach linear modeling.
• Encouragement to like and subscribe for more statistical content.

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