Mathematical Sciences: Bayesian Statistical Theory and      Methodology
{'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 proposed research on Bayesian statistical theory and                methods falls into five categories:  (1)  The development of                    appropriate statistical models and methodology for the analysis                 of selected data and other types of non-random samples often                    encountered.  Particular emphasis will be placed on comparing the               information in a selection sample with that in an unrestricted                  random sample.  (2)  Problems of combining expert opinions,                     including a formal study of how the opinions that experts report                might differ from their true opinions, and the development of                   conditions for determing whether a given rule of combination is                 an admissible one.  (3)  Problems of optimal inspection and                     control in which it is necessary to schedule a sequence of costly               inspections of a stochastic process in order to detect as quickly               as possible the event that the process has gone out of control or               has entered some desirable target area.  (4)  Problems of optimal               stopping in which each member of a team of decision makers                      receives only partial information about the observations in a                   sequential sample, they connot communicate directly with each                   other, and they must develop an optimal strategy for jointly                    stopping the process.  (5)  The development of appropriate                      Bayesian statistical methods for use in various legal settings.                         This research is in the general area of Bayesian statistics,            an approach to statistical decision making that incorporates the                pertinent knowledge that exists prior to the collection of new                  data bearing on the issue.  The general theory behind this                      approach is not new, but has been of little practical use to                    date.  This is because the mathematical equations that need to be               solved to use even a little bit of prior information require                    large amounts of computational horsepower and clever algorithms,                and, until recently, such computational resources have not been                 generally available.  With the required resources becoming more                 commonly available, the potential impact of this research grows.                As pathbreaking Bayesian methods for specific practical examples                are developed, as is proposed here, this approach is expected to                spread and to revolutionize the way data is used to make                        decisions.
{'FirstName': 'Joseph', 'LastName': 'Kadane', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Joseph B Kadane', 'EmailAddress': 'kadane@stat.cmu.edu', 'NSF_ID': '000162372', 'StartDate': '07/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Carnegie-Mellon University', 'CityName': 'PITTSBURGH', 'ZipCode': '152133815', 'PhoneNumber': '4122688746', 'StreetAddress': '5000 FORBES AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '18', 'CONGRESS_DISTRICT_ORG': 'PA18', 'ORG_UEI_NUM': 'U3NKNFLNQ613', 'ORG_LGL_BUS_NAME': 'CARNEGIE MELLON UNIVERSITY', 'ORG_PRNT_UEI_NUM': 'U3NKNFLNQ613'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'Code': '1269', 'Text': 'STATISTICS'}, {'Code': '1333', 'Text': 'Methodology, Measuremt & Stats'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8701770.xml'}