Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection
This work addresses model selection uncertainty in applied Bayesian statistics, particularly for complex real-world problems like epidemiological modeling, though it is incremental as it builds on existing thermodynamic integration methods.
The paper tackles the problem of Bayesian model selection for high-dimensional, analytically intractable distributions by applying referenced thermodynamic integration to compute normalizing constants efficiently, demonstrating favorable convergence performance in benchmarks and successfully applying it to a 200D COVID-19 transmission model in South Korea.
Model selection is a fundamental part of the applied Bayesian statistical methodology. Metrics such as the Akaike Information Criterion are commonly used in practice to select models but do not incorporate the uncertainty of the models' parameters and can give misleading choices. One approach that uses the full posterior distribution is to compute the ratio of two models' normalising constants, known as the Bayes factor. Often in realistic problems, this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochastic methods such as thermodynamic integration (TI). In this paper we apply a variation of the TI method, referred to as referenced TI, which computes a single model's normalising constant in an efficient way by using a judiciously chosen reference density. The advantages of the approach and theoretical considerations are set out, along with explicit pedagogical 1 and 2D examples. Benchmarking is presented with comparable methods and we find favourable convergence performance. The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.