Abstract: The theory and parameters learning of MLNs are introduced, and a parameter learning method based on posterior is proposed. With normal distribution as the prior and pseudo likelihood instead of likelihood, the pseudoposterior is maximized to learn parameters. Experimental results show MLNs pa rameters can be effectively learned, and the inference with the learned model is better that those with current parameter learning methods.

Key words: statistical relational learning, firstorder logic, Markov network, machine learning, Markov logic network