Soft Evidential Update via Markov Chain Monte Carlo Inference

The key task in probabilistic reasoning is to appropriately update one’s beliefs as one obtains new information in the form of evidence. In many application settings, however, the evidence we obtain as input to an inference problem may be uncertain (e.g. owing to unreliable mechanisms with which we obtain the evidence) or may correspond to (soft) degrees of belief rather than hard logical facts. So far, methods for updating beliefs in the light of soft evidence have been centred around the iterative proportional fitting procedure and variations thereof. In this work, we propose a Markov chain Monte Carlo method that allows to directly integrate soft evidence into the inference procedure without generating substantial computational overhead. Within the framework of Markov logic networks, we demonstrate the potential benefit of this method over standard approaches in a series of experiments on synthetic and real-world applications.