The classical approach to allocation optimization discussed in the second part of the book assumes that the distribution of the market is known. The samplebased allocation, discussed in the previous chapter, is a two-step process: first the market distribution is estimated and then the estimate is inputted in the classical allocation optimization problem. Since this process leverages the estimation error, portfolio managers mistrust these two-step “optimal” approaches and prefer to resort to ad-hoc recipes, or trust their prior knowledge/experience. In this chapter we discuss allocation strategies that account for estimation risk within the allocation decision process. These strategies must be optimal : overall opportunity cost of these strategies must be as low as possible. The main reasons why estimation risk plays such an important role in financial applications is the extreme sensitivity of the optimal allocation function to the unknown parameters that determine the distribution of the market. In this section, we use the Bayesian approach to estimation to limit this sensitivity. We present Bayesian allocations in terms of the predictive distribution of the market, as well as the classical-equivalent Bayesian allocation, which relies on Bayes-Stein shrinkage estimators of the market parameters. The Bayesian approach provides a mechanism that mixes the positive features of the prior allocation and the sample-based allocation: the estimate of the market is shrunk towards the investor’s prior in a self-adjusting way and the overall opportunity cost is reduced.