This paper deals with data (or information) fusionfor the purpose of estimation. Three estimation fusion architecturesare considered: centralized, distributed, and hybrid. A unifiedlinear model and a general framework for these three architecturesare established. Optimal fusion rules based on the best linearunbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete,incomplete, or no prior information. These rules are moregeneral and flexible, and have wider applicability than previous results.For example, they are in a unified form that is optimal for allof the three fusion architectures with arbitrary correlation of localestimates or observation errors across sensors or across time. Theyare also in explicit forms convenient for implementation. The optimalfusion rules presented are not limited to linear data models.Illustrative numerical results are provided to verify the fusion rulesand demonstrate how these fusion rules can be used in cases withcomplete, incomplete, or no prior information.