Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation

Since the simple bootstrap approach yields an extremely computationally demanding algorithm, we investigate the use of existing techniques for the design of a more efficient proposal density. We propose combining the Laplace approximation and various population Monte Carlo methods. Data from an existing monitoring network were used to calibrate relevant parts of the model.

Performance of the methods in a real radiation emergency situation is evaluated in a simulated experiment due to the lack of real data. The proposed tailor-made proposal was found to be much more computationally efficient than previously published methods. The adaptive Monte Carlo methods thus represent a compelling computational approach for the evaluation of probabilistic environmental models.

The data used and a Python implementation of the methods are available as supplementary material online.