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Prior model probabilities

Dear JASP community,

I am writing to gain clarity about setting prior model probabilities. In Wagenmakers et al. (2018), the authors state "P(M) indicates prior model probabilities (which the current version of JASP sets to be equal across all models at hand)." However, in the current version, when I run my own Bayes regression for the Auction data, I find that P(M) has variable values


QUESTION: Do users have the ability to set P(M) or is it automatically generated by JASP? How were the priors set to have variable values?


Many thanks,

Caroline

Comments

  • Hi Caroline,

    Basically, there are two default ways to set the prior model probabilities. One is uniform (basically assuming that every predictor has probability 0.5 of being included; this does lead to a prior preference for models with 50% of the predictors included), the other is based on a more complicated method by Scott and Berger (2006, 2010) which results in a uniform prior across model classes of different numbers of predictors. The model probabilities you see are the Scott and Berger ones. You can specify which one you prefer in the JASP GUI. We hope to present a paper shortly that will explain this in more detail.

    Cheers,

    E.J.

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