Within the Bayesian paradigm for statistics, posterior probability distributions for variables of interest are computed based on fully specified stochastic models, which may be described in the form of a Bayesian network. For some simple networks, exact inference is possible, but in many cases, numerical methods must be used, or one must resort to MCMC simulation. However, exact bounds for the accuracy of results from MCMC simulations are often not available. In forensic applications of Bayesian networks, this can be a particular problem. In this project, we will develop inference methods for ILDI (Inference with Low Dimensional Integration) networks, using numerical integration in such a way that precise bounds for the accuracy of results are obtained. ILDI networks contain many of the types of models we see in forensic sciences. In a cooperation between the Swedish National Laboratory for Forensic Science, the National Veterinary Institute in Sweden, and Chalmers University, we will also work with a number of example forensic applications, aiming to study and support the general process of Bayesian network model building and utilization.
Docent vid Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Doktorand vid Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Funding Chalmers participation during 2012–2015