Relational Continuous Models (RCMs) represent joint probability densities
over attributes of objects, when the attributes have continuous domains. With
relational representations, they can model joint probability distributions over
large numbers of variables compactly in a natural way. This paper presents a
new exact lifted inference algorithm for RCMs, thus it scales up to large
models of real world applications. The algorithm applies to Relational Pairwise
Models which are (relational) products of potentials of arity 2. Our algorithm
is unique in two ways. First, it substantially improves the efficiency of
lifted inference with variables of continuous domains. When a relational model
has Gaussian potentials, it takes only linear-time compared to cubic time of
previous methods. Second, it is the first exact inference algorithm which
handles RCMs in a lifted way. The algorithm is illustrated over an example from
econometrics. Experimental results show that our algorithm outperforms both a
groundlevel inference algorithm and an algorithm built with previously-known
lifted methods.

本文提出新的精确的提升推理算法，用于关系连续模型，能够有效地推断大规模真实应用的模型；该算法处理连续域变量时，改进了传统方法的效率，在高斯概率下算法时间复杂度达到线性级别，并且是第一个通过提升方式处理关系连续模型的精确推理算法；文章通过实验验证表明，该算法性能优于基础推理算法和其他已知的提升方法构建的算法。