The　probability　distribution　of　random　variables　is　usually　incomplete　owing　to　a　limited　amount　of　data,　making　it　a　challenging　problem　for　structural　reliability　assessments.　This　paper　presents　a　novel　reliability　analysis　method　for　civil　structures　with　incomplete　data.　The　probability　distributions　of　the　random　variables　were　estimated　using　an　improved　Bayesian　inference　method.　In　the　proposed　inference　method,　the　frequencies　and　mode　shapes　were　selected　as　the　observation　data.　The　likelihood　function　is　replaced　by　homogeneous　chaos,　and　the　computational　efficiency　of　Bayesian　inference　is　improved.　The　affine　invariant　ensemble　algorithm　was　used　to　generate　samplers　for　the　posterior　distribution.　After　estimating　the　probability　distribution　of　the　incomplete　random　variables,　a　reliability　analysis　was　performed,　and　the　failure　probability　of　the　structure　was　estimated.　Numerical　studies　on　a　two-span,　two-story　reinforced　concrete　building　and　a　five-span　bridge　are　presented.　Experimental　studies　were　also　performed　on　an　eight-story　steel　frame　structure.　The　results　show　that　the　probability　distributions　of　random　variables　can　be　significantly　updated　using　the　proposed　technique　and　modal　properties.　The　proposed　technique　was　more　efficient　than　the　Gibbs　sampling　method.　The　failure　probabilities　of　the　structures　were　estimated　using　the　estimated　probability　distributions.