Mathematical Sciences: Methodological and Computational     Aspects of Statistical Modeling and Inference
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{'SignBlockName': 'Alan Izenman', 'PO_EMAI': None, 'PO_PHON': None}
This work will focus on three areas of theoretical                         statistics:  (1)  Bayesian modelling and analysis of time series,               including time domain and frequency domain approaches to both                   long memory and short memory models, (2)  other Bayesian projects               including nonlinear regression models and measurement error                     models, and (3) Bayes causal networks and belief functions,                     including mathematical characterization of these new models, as                 well as the development of principles for model construction and                computation that are required for practical use.                                     Dempster's work in theoretical statistics is becoming                      foundational for other scientists attempting to incorporate the                 realities of uncertainty into expert computer systems.  In                      addition to extending relevant theory, he will tackle the                       computational bottleneck that now blocks successful widespread                  implementation of these models.
{'FirstName': 'Arthur', 'LastName': 'Dempster', 'PI_MID_INIT': 'P', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Arthur P Dempster', 'EmailAddress': 'dempster@fas.harvard.edu', 'NSF_ID': '000190910', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '1269', 'Text': 'STATISTICS'}
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