Aiming　at　the　problem　that　the　current　estimation　methods　of　rock　shear　strength　parameters　cannot　reflect　and　quantify　their　uncertainty,　an　estimation　method　based　on　gene　expression　programming　(GEP)　and　Bayesian　inference　(BI)　is　proposed　to　realize　uncertainty　prediction　in　the　sense　of　probability.　Based　on　the　rock　strength　parameter　dataset,　GEP　was　used　to　establish　the　mapping　relationship　between　rock　shear　strength　parameters　and　uniaxial　compressive　strength　(UCS),　and　tensile　strength　(UTS).　Then,　according　to　the　function　expression,　prior　information,　and　the　likelihood　function,　the　joint　posterior　probability　distribution　of　the　rock　shear　strength　parameters　was　obtained　by　BI.　The　results　showed　that　the　GEP-BI　method　can　effectively　predict　the　shear　strength　parameters　under　the　given　UCS　and　UTS　of　rock　experimental　data,　and　can　also　give　the　probability　distribution　of　the　predicted　results,　which　has　strong　interpretability　and　uncertainty　analysis　ability.　According　to　the　uncertainty　degree　and　prediction　effectiveness　of　the　results,　it　was　suggested　to　adopt　the　lognormal　distribution　as　the　likelihood　function,　which　can　effectively　avoid　the　meaningless　negative　value.　Compared　with　machine　learning　methods,　the　GEP-BI　model　has　a　better　prediction　effect,　which　proves　the　feasibility　and　effectiveness　of　the　GEP-BI　method.　©　2025　The　Authors