  Data collection in economically constrained countries often necessitates
using approximate and biased measurements due to the low-cost of the sensors
used. This leads to potentially invalid predictions and poor policies or
decision making. This is especially an issue if methods from resource-rich
regions are applied without handling these additional constraints. In this
paper we show, through the use of an air pollution network example, how using
probabilistic machine learning can mitigate some of the technical constraints.
Specifically we experiment with modelling the calibration for individual
sensors as either distributions or Gaussian processes over time, and discuss
the wider issues around the decision process.
