A　stochastic　model　updating　method　seeking　for　the　probabilistic　properties　of　uncertain　parameters　is　proposed　that　works　better　with　real-world　cases.　For　simplification,　the　stochastic　model　updating　process　is　decomposed　into　a　series　of　deterministic　ones.　The　Monte　Carlo　simulation　is　employed　to　generate　response　samples,　then　combined　with　the　fast-computation　feature　of　the　response　surface　method.　An　inverse　optimization　problem　is　established　for　predicting　parameters　corresponding　to　each　sample.　Then,　the　mean　and　variance　of　each　parameter　can　be　statistically　estimated　based　on　the　numerous　sample　predictions.　The　proposed　method　has　been　validated　using　a　set　of　tested　metal　plates,　and　the　means　and　variances　of　the　thicknesses　and　material　parameters　are　well　identified.　The　proposed　method　has　been　validated　in　order　to　demonstrate　its　feasibility　and　reliability.