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In this paper, we study the mean-variance portfolio selection problem underpartial information with drift uncertainty. First we show that the market modelis complete even in this case while the information is not complete and thedrift is uncertain. Then, the optimal strategy based on partial information isderived, which reduces to solving a related backward stochastic differentialequation (BSDE). Finally, we propose an efficient numerical scheme toapproximate the optimal portfolio that is the solution of the BSDE mentionedabove. Malliavin calculus and the particle representation play important rolesin this scheme.

Mean-variance portfolio selection under partial information with drift uncertainty