Bayesian Reconstruction of Missing Observations
This work addresses missing data reconstruction in traffic engineering, but it appears incremental as it applies a known Bayesian framework to a specific domain without claiming major breakthroughs.
The paper tackles the problem of missing data interpolation by introducing Bayesian reconstruction, a probabilistic alternative to deterministic methods, and applies it to traffic data reconstruction, evaluating its statistical performance using a statistical mechanical approach.
We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically using a Bayesian treatment. In this paper, we address the framework of Bayesian reconstruction and its application to the traffic data reconstruction problem in the field of traffic engineering. In the latter part of this paper, we describe the evaluation of the statistical performance of our Bayesian traffic reconstruction model using a statistical mechanical approach and clarify its statistical behavior.