Common use of Prior selection Clause in Contracts

Prior selection. As for priors, we used weakly informative priors in our models similar to the ones provided in ▇▇▇▇ Wiki on Github (▇▇▇▇▇▇▇▇▇▇ et al., 2020). In their blogpost, ▇▇▇▇▇▇▇▇▇▇ et al. (2020) explains five different levels of priors: (i) flat priors, (ii) super-vague priors, (iii) weakly informative priors, (iv) generic weakly informative priors, (v) specific informative priors. In this context, a prior is considered informative or weak depending on its effect on the likelihood. Suppose the likelihood dominates the results, and the effect of a prior is either zero or unnoticeable. In that case, the prior is not informative. In our case, we chose priors that diminish the probability space fairly. We used a Normal(0,1) prior for the intercept and a Normal(0,1) prior for most of the slopes except for ungrammaticality and the interaction between ungrammaticality and the plural attractor. We set a Normal(-4,1) prior for the ungrammaticality and a Normal(1,0.5) prior for the interaction between the ungrammaticality and the plural attractor. These priors were set following previous findings. Lastly, Cauchy+(0,1) prior that is truncated at 0 for the standard deviations of random effects, and a LKJ(2) prior for correlation matrix for the random effects are used.

Prior selection. As for priors, we used weakly informative priors in our models similar to the ones provided in ▇▇▇▇ Wiki on Github (▇▇▇▇▇▇▇▇▇▇ et al., 2020). In their blogpost, ▇▇▇▇▇▇▇▇▇▇ et al. (2020) explains five different levels of priors: (i) flat priors, (ii) super-vague priors, (iii) weakly informative priors, (iv) generic weakly informative priors, (v) specific informative priors. In this context, a prior is considered informative or weak depending on its effect on the likelihood. Suppose the likelihood dominates the results, and the effect of a prior is either zero or unnoticeable. In that case, the prior is not informative. In our case, we chose priors that diminish the probability space fairly. We used a Normal(0,1) prior for the intercept and a Normal(0,1) prior for most of the slopes except for ungrammaticality and the interaction between ungrammaticality and the plural attractor. We set a Normal(-4,1) prior for the ungrammaticality and a Normal(1,0.5) prior for the interaction between the ungrammaticality and the plural attractor. These priors were set following previous findings. Lastly, Cauchy+(0,1) prior that is truncated at 0 for the standard deviations of random effects, and a LKJ(2) prior for correlation matrix for the random effects are used.a