Sorry, I don't have time to go into this now, but as far as I remember, 
there are 2 main techniques to solve ARD regression:
- EM, which guarantees to increase the criterion at each iteration, but 
generally slow
- gradient descent, which has no guarantee, but is generally faster.
Maybe the algorithm here is gradient descent ?

My 2 cents,

Bertrand


On 01/17/2012 06:56 AM, Shiqiao Du (杜 世橋) wrote:
> I'd like to ask why `BayesianRidge` and `ARDRegression` do not use
> marginal log likelihood (MLL) but learned coefficients to check
> convergence when fitting.
> I know that most iterative algorithms must have some objective
> function by which the convergence is checked.
> In Bayesian inference, like variational learning, the objective function is 
> MLL.
> Are there any reason not to use  MLL?
>
> And also, is the learning algorithm of `BayesianRidge` and
> `ARDRegression` a kind of variational learning?
> If so, the MLL is ensured to increase upon learning.
> However, the MLL of ARDRegression
> (http://scikit-learn.org/stable/_images/plot_ard_3.png) did decrease.
> Is this a bug or did I misunderstand something?
>
> ---------------------------------------------------------------
> 杜 世橋 (Shiqiao Du)
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> Twitter http://twitter.com/lucidfrontier45
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