I guess I'm not really appreciating the difference between node value > prior and progressive bias - adding a fixed small number of wins or > diminishing heuristic value seems very similar to me in practice. Is the > difference noticeable? >
It just means that the weight of the prior does not necessarily decrease linearly. For example, for Rave, you might prefer to have a weight which depends on both the number of Rave simulations and on the number of "real" simulations. The formula designed by Sylvain for Rave, for example, can't be recovered without this generalization. For the current mogo, as we have several terms (one for the empirical success rate, one for Rave, one for patterns, one for expert rules...), some of them with used at different places in the code (there is a prior expressed in terms of "rave" simulations, and a prior expressed as "real" simulations), it would be complicated to come back to something much simpler ... but perhaps it's possible. Such a clarification might be useful, I have no clear idea of the impact of Rave values now in mogo, in particular for long time settings, and it's not so easy to clarify this point - too many dependencies between the many terms we have. I think someone pointed out a long time ago on this mailing list that initializing the prior in terms of Rave simulations was far less efficient than initializing the prior in terms of "real" simulations, at least if you have classical "rave" formulas - at least, we had an improvement when adding a prior in the "real" simulations, but we had also an improvement when adding one more term, which is not linear. Sorry for forgetting who :-( Olivier
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