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Machine Learning and Deep Learning

557From Frequentist to Bayesian Perspective
558Prior Distributions on Weights
559Likelihood Function for Regression
560Bayesian Inference via Bayes' Rule
561Conjugate Priors and Analytical Posteriors
562Posterior Predictive Distribution
563Maximum A Posteriori Estimation
564Hyperparameters and Evidence Approximation
565Implementing Bayesian Linear Regression
566When to Use Bayesian Regression
567From Linear Regression to Gaussian Processes
568Kernel Functions and the Covariance Matrix
569Common Kernel Functions: RBF, Matérn, and Periodic
570Kernel Composition and Design
571GP Prior: Sampling Functions
572GP Posterior: Conditioning on Data
573GP Prediction: Mean and Uncertainty
574Hyperparameter Optimization via Marginal Likelihood
575Computational Complexity and Scalability Issues
576Sparse Gaussian Processes and Inducing Points
577GPs for Classification
578Implementing GPs with GPyTorch or scikit-learn
579Exact Inference: Marginalization and Conditioning
580Conjugate Priors and Analytical Posteriors
581Limitations of Exact Inference
582Monte Carlo Integration Fundamentals
583Markov Chain Monte Carlo: The Metropolis-Hastings Algorithm
584Gibbs Sampling for Conditional Distributions
585Diagnosing MCMC Convergence
586Variational Inference: Approximating Posteriors
587Mean-Field Variational Inference
588Comparing Inference Methods: Trade-offs and Use Cases

Machine Learning and Deep Learning

557From Frequentist to Bayesian Perspective
558Prior Distributions on Weights
559Likelihood Function for Regression
560Bayesian Inference via Bayes' Rule
561Conjugate Priors and Analytical Posteriors
562Posterior Predictive Distribution
563Maximum A Posteriori Estimation
564Hyperparameters and Evidence Approximation
565Implementing Bayesian Linear Regression
566When to Use Bayesian Regression
567From Linear Regression to Gaussian Processes
568Kernel Functions and the Covariance Matrix
569Common Kernel Functions: RBF, Matérn, and Periodic
570Kernel Composition and Design
571GP Prior: Sampling Functions
572GP Posterior: Conditioning on Data
573GP Prediction: Mean and Uncertainty
574Hyperparameter Optimization via Marginal Likelihood
575Computational Complexity and Scalability Issues
576Sparse Gaussian Processes and Inducing Points
577GPs for Classification
578Implementing GPs with GPyTorch or scikit-learn
579Exact Inference: Marginalization and Conditioning
580Conjugate Priors and Analytical Posteriors
581Limitations of Exact Inference
582Monte Carlo Integration Fundamentals
583Markov Chain Monte Carlo: The Metropolis-Hastings Algorithm
584Gibbs Sampling for Conditional Distributions
585Diagnosing MCMC Convergence
586Variational Inference: Approximating Posteriors
587Mean-Field Variational Inference
588Comparing Inference Methods: Trade-offs and Use Cases

← Machine Learning and Deep Learning

Lesson 559 of 3,538·14. Probabilistic and Bayesian Machine LearningPro lesson

Likelihood Function for Regression

Express the probability of observed data given parameters; connect to the Gaussian noise assumption in linear regression.

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