On Approximate Inference of Dynamic Latent Classification Models for Oil Drilling Monitoring

We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Monitoring in such a complex domain is a challenging task. Not only is such a domain typically volatile and following non-linear dynamics, but sensor input to the monitoring system can also often be high dimensional, making it difficult to model and classify the domain’s states. Dynamic latent classification models are dynamic Bayesian networks capable of effective and efficient modeling and classification. An approximate inference algorithm utilizing Gaussian collapse has been tailormade for this family of models, but the approximation’s properties have not been fully explored. In this paper we compare alternatives approximate inference methods for the dynamic latent classification model, in particular focusing on traditional sampling techniques. We show that the approximate scheme based on Gaussian collapse is computationally more efficient than sampling, while offering comparable accuracy results.