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We study the task of retrieving relevant experiments given a queryexperiment. By experiment, we mean a collection of measurements from a set of`covariates' and the associated `outcomes'. While similar experiments can beretrieved by comparing available `annotations', this approach ignores thevaluable information available in the measurements themselves. To incorporatethis information in the retrieval task, we suggest employing a retrieval metricthat utilizes probabilistic models learned from the measurements. We argue thatsuch a metric is a sensible measure of similarity between two experiments sinceit permits inclusion of experiment-specific prior knowledge. However, accuratemodels are often not analytical, and one must resort to storing posteriorsamples which demands considerable resources. Therefore, we study strategies toselect informative posterior samples to reduce the computational load whilemaintaining the retrieval performance. We demonstrate the efficacy of ourapproach on simulated data with simple linear regression as the models, andreal world datasets.

Retrieval of Experiments by Efficient Estimation of Marginal Likelihood