A consensus algorithm extracts a consensus from an underlying model of data.
This consensus includes a model of the data and which datapoints fit the model.
An Estimator is able to create a model that best fits a set of data.
It is also able to determine the residual error each data point contributes in relation to the model.
See Consensus. A multi-consensus can handle situations where different subsets of the data are consistent
with different models. This kind of consensus also considers whether a point is part of another orthogonal
model that is known before assuming it is a true outlier. In this situation there are inliers of different
models and then true outliers that are actual erroneous data that should be filtered out.