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Publication Detail
Retrieval of experiments by efficient comparison of marginal likelihoods
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Seth S, Shawe-Taylor J, Kaski S
  • Publication date:
    01/01/2014
  • Pagination:
    135, 142
  • Journal:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    8835
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
© Springer International Publishing Switzerland 2014. We study the task of retrieving relevant experiments given a query experiment. By experiment, we mean a collection of measurements from a set of ‘covariates’ and the associated ‘outcomes’. While similar experiments can be retrieved by comparing available ‘annotations’, this approach ignores the valuable information available in the measurements themselves. To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements.We argue that such a metric is a sensible measure of similarity between two experiments since it permits inclusion of experiment-specific prior knowledge. However, accurate models are often not analytical, and one must resort to storing posterior samples which demands considerable resources. Therefore, we study strategies to select informative posterior samples to reduce the computational load while maintaining the retrieval performance. We demonstrate the efficacy of our approach on simulated data with simple linear regression as the models, and real world datasets.
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