symmetric maximum likelihood corresponds to minimizing the KL divergence with the data on the left and the model on the right but we can actually flip that around we can minimize the KL divergence with the model on the left and the data on the right and when we do that we get a different result where instead of averaging out the two modes the model as shown in the panel on the right here we'll choose one of the modes we can think of KL data come a model as saying that the model should put probability mass everywhere that the data puts