In this talk we present a method for reliability analysis based on a limited number observations of the uncertain parameter of a computational model. Rather than fitting a crisp distribution to the data, we use a probability box (p-box) of sliced-normal distributions spanning between the maximum likelihood estimate and the moment-bounded maximum entropy estimate. Sliced-normals are capable of characterizing multivariate distributions exhibiting complex parameter dependencies with minimal modeling effort. This p-box, which accounts for the effects of having a limited amount of data on the resulting reliability analysis, approaches a single distribution as the number of observations increases.  Furthermore, we identify the slice-normal distributions leading to the maximal and the minimal failure probability estimates thereby complementing a point estimate with a bounding range.