Bayesian　updating　reduces　epistemic　uncertainty　for　more　reliable　predictions,　but　characterizing　the　distribution　of　conditional　failure　probability　with　measurement　data　is　complex.　This　study　proposes　an　efficient　and　accurate　method　to　fully　describe　the　probabilistic　characteristics　of　the　updated　conditional　failure　probability.　It　formulates　the　first　three　raw　moments　of　the　updated　conditional　reliability　index　and　uses　weighted　sparse　grid　numerical　integration　to　evaluate　these　moments.　A　shifted　lognormal　distribution　is　then　used　to　approximate　the　probability　density　function　of　the　updated　conditional　reliability　index,　allowing　for　the　determination　of　the　mean,　quantiles,　and　distribution　of　the　updated　conditional　failure　probability　with　information　reuse.　An　illustrative　example　was　conducted　to　demonstrate　the　method＇s　performance,　with　results　compared　against　benchmarks　from　MCMC　combined　with　MCS.　©　2024　Proceedings　of　ISMA　2024　-　International　Conference　on　Noise　and　Vibration　Engineering　and　USD　2024　-　International　Conference　on　Uncertainty　in　Structural　Dynamics.　All　rights　reserved.