Mathematical Sciences: Optimal Bayesian Design for          Non-Linear Problems
{'Code': '03040107', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alan Izenman', 'PO_EMAI': None, 'PO_PHON': None}
The goal of this research is to provide the theory and the              methods for finding efficient experimental designs for non-linear               problems.  In the non-linear case, the efficiency of a design                   depends on the values of the unknown parameters and a traditional               approach to the problem has been to design for a best guess of                  the parameters.  A Bayesian approach uses a prior distribution                  and takes into account not only the best guess but also the                     uncertainty associated with the best guess.  Bayesian designs can               also be interpreted in a non-Bayesian framework as designs that                 minimize the average asymptotic variances.  The authors will                    extend their results on the important special problem of                        designing an experiment where the response increases with dose                  according to a logistic distribution.  The primary tools they                   must further develop in order to obtain successful extensions are               optimization algorithms.   They first intend to work on                         sequential designs.                                                                     This research will permit more information to be                        incorporated into the design of efficient experiments.  Inproved                computational capabilities over the last decade makes this work                 possible.  Prior to the computer revolution, scientific                         experiments were simply designed with computational feasibility a               major consideration.  Not only has this limitation been largely                 eliminated, but also larger and more complex experiments are                    being conducted.  Efficient designs for modern science are                      critical for sound utilization of the nation's research and                     development funds.
[{'FirstName': 'Kinley', 'LastName': 'Larntz', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kinley Larntz', 'EmailAddress': None, 'NSF_ID': '000448736', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}, {'FirstName': 'Kathryn', 'LastName': 'Chaloner', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Kathryn Chaloner', 'EmailAddress': 'kathryn@stat.umn.edu', 'NSF_ID': '000217678', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}]
{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'ZipCode': '554552009', 'PhoneNumber': '6126245599', 'StreetAddress': '200 OAK ST SE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Minnesota', 'StateCode': 'MN', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MN05', 'ORG_UEI_NUM': 'KABJZBBJ4B54', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF MINNESOTA', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1269', 'Text': 'STATISTICS'}
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