The shear wave velocity 𝑉 𝑆 is of interest since it can provide information related to soil smallstrain stiffness characteristics and site classification. However, it is difficult to measure 𝑉 𝑆 directly without special equipment, particularly in smaller and medium-sized projects. Thus, numerous studies are conducted on V-S correlating, and extensive research has demonstrated that cone penetration test (CPT) and standard penetration test (SPT) data have a good relationship with shear wave velocity. Due to the uncertainty of the transformation model, such 𝑉 𝑆 values calculated by the empirical equation are unsatisfactory. The purpose of the present paper is to propose a Bayesian framework for determining the probabilistic characteristics of 𝑉 𝑆 in the context of transformation uncertainty. In addition, the Bayesian framework considers both in situ test data (SPT, CPT) and prior information. Results indicate that the Bayesian framework considering two in-situ tests accurately predicts the shear wave velocity. There are several advantages of using the Bayesian method described in this study. 1. The Bayesian framework incorporates both the inherent uncertainty of the shear wave velocity and the transformation uncertainty.2. Prior information and field data can be combined. 3. In the framework, statistical characteristics of the 𝑉 𝑆 can be ascertained from small samples of field test data.