This chapter gives an overview of ensemble learning with emphasis on solutions yielding linear computational complexity. More comprehensive introductions to ensemble learning can be found for instance in [61,34,44].
In generative models, inference can be done by estimating how likely a set of parameter values could have produced the observed data. Basically one wants to find a representative of the probability mass in the parameter space, but there are different methods for doing it in practice.