Talk:Bayesian linear regression

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This isn't a description of Bayesian linear regression. It's a description of the Empirical Bayes approach to linear regression rather than a full Bayesian approach. (Empirical Bayes methods peek at the data to ease the computational burden.) Not only that, it assumes a natural conjugate prior, which is a reasonable approach in many cases, but it is a serious constraint. The fully Bayesian approach with non-conjugate priors is nowadays tractable in all but the largest models through the use of Markov chain Monte Carlo techniques. In my view, this article represents a particular and rather dated approach. Blaise 21:53, 10 April 2007 (UTC)

Can you fix it and check my rating? Thanks - Geometry guy 14:19, 13 May 2007 (UTC)

Some questions

I've got some questions about this article. What this method gives you is a weighted combination of some prior slope, and a new slope estimate from new data.

Q1) the weights are determined by the A. In this one-dimensional case I presume this is just a variance. There are no details as to how this A would be calculated or estimated. Does anyone know?

Q2) I could set this problem up using classical statistics, I think. I'd say "let's make a prediction based on a weighted combination of the prior slope and the new slope". Then I'd do some algebra to derive an expression for the weight. Does anyone have any idea whether the final answer would be much, if any, different?

thanks

82.44.214.29 20:09, 14 October 2007 (UTC)