  Nowadays model uncertainty has become one of the most important problems in
both academia and industry. In this paper, we mainly consider the scenario in
which we have a common model set used for model averaging instead of selecting
a single final model via a model selection procedure to account for this
model's uncertainty to improve the reliability and accuracy of inferences. Here
one main challenge is to learn the prior over the model set. To tackle this
problem, we propose two data-based algorithms to get proper priors for model
averaging. One is for meta-learner, the analysts should use historical similar
tasks to extract the information about the prior. The other one is for
base-learner, a subsampling method is used to deal with the data step by step.
Theoretically, an upper bound of risk for our algorithm is presented to
guarantee the performance of the worst situation. In practice, both methods
perform well in simulations and real data studies, especially with poor-quality
data.
