ABSTRACT. t. Constrained counting is important in domains ranging from artificial intelligence to software analysis. There are already a few approaches for counting models over various types of constraints. Recently, hashing-based approaches achieve success but still rely on solution enumeration. In the IJCAR version, a probabilistic approximate model counter is proposed, which is also a hashingbased universal framework, but with only satisfiability queries. A dynamic stopping criteria, for the algorithm, is presented, which has not been studied yet in previous works of hashing-based approaches. Although the algorithm lacks theoretical guarantee, it works well in practice. In this paper, we further extend our approach to SMT(LIA) formulas with a new encoding technique.