方法对比

	拉普拉斯近似	Bayesian Regression	期望传播 (EP)
领域	贝叶斯	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods	Bayesian methods
起源年份	1986	—	2001
提出者	Pierre-Simon Laplace (1774); Bayesian formalisation: Tierney & Kadane (1986)	—	Thomas P. Minka
类型	Analytical posterior approximation	Bayesian linear model	Approximate inference algorithm
开创性文献	Tierney, L. & Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association, 81(393), 82–86.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann.
别名	Laplace's method, saddle-point approximation (Bayesian), second-order Gaussian approximation, LA	bayesian linear regression, probabilistic regression, bayesian regresyon	EP, expectation propagation, EP algorithm, assumed-density filtering generalisation
相关	3	2	3
摘要	The Laplace approximation is a classical analytic technique that replaces an intractable posterior distribution with a multivariate Gaussian centred at the posterior mode, using the curvature of the log-posterior at that mode to set the covariance. Formalised for Bayesian statistics by Tierney and Kadane (1986) in their landmark Journal of the American Statistical Association paper, it provides a fast, deterministic alternative to Markov chain Monte Carlo and forms the mathematical core of Integrated Nested Laplace Approximations (INLA).	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Expectation Propagation (EP) is a deterministic message-passing algorithm for approximate posterior inference in Bayesian models, introduced by Thomas P. Minka at UAI 2001. It iteratively refines a set of local approximate factors — each drawn from the exponential family — so that their product closely matches the true intractable posterior, achieving higher accuracy than mean-field variational inference on many probabilistic machine learning tasks.