Black-Litterman vs. Resampled efficient frontier

Can I say the major difference is Black-Litterman allowing to incorporate investor’s view?

According to Reading 18 Asset Allocation of the CFAI text (pages 230-238), resampled efficient frontier and the Black-Litterman approach are both offered as two alternatives to mean-variance optimization for the purposes of asset allocation.

Resampled efficient frontier involves running a series of simulations (based on sample values of asset class means, variances, and covariances ) and mean-variance optimization is used to obtain portfolio weights for a number of efficient portfolios. Average weights are then found for each asset class in the simulated efficient portfolios. This information, along with other information from the simulated efficient portfolios, is then integrated into a single frontier known as the resampled efficient frontier.

The Black-Litterman approach is a quantitative approach, similar to the resampled efficient frontier, but this appoach improves upon mean-variance optimization by combining an investor’s own views on expected returns with a set of expected asset-class returns to determine an asset allocation strategy.

Additionally, I found a linkto a prior post in this forum that has a good explanation of the difference between the two approaches. The comment by ‘janakisri’ within the post is the most helpful, in my opinion.

Hope this helps!