Arthur P. Dempster contributed to multivariate analysis, incomplete-data inference and the representation of uncertain evidence. With Nan Laird and Donald Rubin, he developed the general expectation-maximization algorithm formulation published in 1977. Its expectation step averages a complete-data log likelihood using the current Conditional distribution of latent or missing variables; its maximization step updates parameters using that average. This makes a difficult incomplete-data likelihood accessible through a sequence of simpler calculations.
Harvard's institutional tribute also traces his work on Dempster-Shafer theory, in which evidence can leave an explicit component of uncertainty about an assertion. His dissertation concerned multivariate settings with more variables than observations, anticipating a central difficulty of high-dimensional statistics.[ref]
https://statistics.fas.harvard.edu/news/2026/06/arthur-pentland-dempster-1929-2026-extended-obituary-and-tribute. Harvard tribute, dissertation, upper/lower probabilities and EM collaboration
Identity and life dates: Harvard Department of Statistics obituary and tribute.
Portrait of Arthur P. Dempster
. Llorenzi. CC BY-SA 3.0, via Wikimedia Commons. Source. Articles by others on the same topic
Arthur P. Dempster is a prominent statistician known for his contributions to the field of statistics, particularly in the areas of Bayesian statistics and the development of methods for data analysis. He is best known for Dempster-Shafer theory, which is a mathematical theory of evidence that generalizes Bayesian probability. This theory provides a framework for dealing with uncertainty and combining evidence from different sources. Dempster has also made significant contributions to statistical inference, multivariate analysis, and computational statistics.