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.
- Pub Date:
- November 2019
- Computer Science - Machine Learning;
- Statistics - Applications;
- Statistics - Machine Learning;
- Presented at NeurIPS 2019 Workshop on Machine Learning for the Developing World