Fast Exact Bayesian Inference for High-Dimensional Models

Fast Exact Bayesian Inference for High-Dimensional Models

Ferreira, J. F., Lanillos, P., & Dias, J. (2015). Fast Exact Bayesian Inference for High-Dimensional Models. In Workshop on Unconventional computing for Bayesian inference (UCBI), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).pdf

Summary:

We present the principles that allow the tractable implementation of exact inference processes concerning a group of widespread classes of Bayesian generative models, which have until recently been deemed as intractable whenever formulated using high-dimensional joint distributions. We will demonstrate the usefulness of such a principled approach with an example of real-time OpenCL implementation using GPUs of a full-fledged, computer vision-based model to estimate gaze direction in human-robot interaction (HRI).