In the modern era with explosive growth of information, it is important to process information in an efficient and meaningful manner. Indeed, collecting together overall information from different studies is a critical component for decision-making. Combined results from multiple studies summarize overall associations, and inferences from the combined results are typically more reliable than inferences from any single study. The study of formal and meaningful ways of combining studies from independent sources is important both theoretically and practically.
The one-and-half day workshop will explore methods of, theory for, and barriers to combining data from multiple sources for improved decision making that exploits inferences that are typically more efficient and potentially more accurate than those from any single source. It will examine timely and important applications from a variety of fields. The workshop will bring together researchers from different disciplines to address issues related to combining information. It will disseminate research results in the areas of model building, Bayesian analysis, meta-analysis, and machine learning, among others.