A method of probabilistic logic reasoning on Bayesian networks
Li Yong · Yunnan Daxue xuebao. Shehui kexue ban · 2009
For processing reasoning and resolving the discrepancy between logic and probability in making inferences from conditional probability information,we propose and implement a probabilistic logic reasoning approach on Bayesian networks,which combings Conditional Event Algebra and Markov Monte Carlo simulating algorithm.By extending normal measurable space with conditional event,we first bring logic consistent with probability in denoting conditional probability information,and then we transform a higher-order conditional event to normal events and correspond logical combination events via Conditional Event Algebra.We use Gibbs simulation to sample the normal events to be a stationary state.By computing the quantitative values of the events,we can evaluate the quantitative value of higher-order conditional event at last.An example of application of our method shows how we make inferences from conditional probability information.