Bayesian Reconstruction of Missing Observations

TL;DR

This paper introduces a Bayesian probabilistic approach for reconstructing missing traffic data, providing a statistical framework and evaluating its performance using statistical mechanics methods.

Contribution

It presents a novel Bayesian reconstruction framework for traffic data, emphasizing probabilistic interpolation and statistical performance analysis.

Findings

Bayesian reconstruction effectively interpolates missing traffic data.

Statistical mechanical approach evaluates model performance.

The method offers probabilistic insights into data reconstruction.

Abstract

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.