To ensure that estimates of risk and reliability inform design and resource allocation decisions

in the development of complex engineering systems, early engagement in the design life cycle is

necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept

development is the limited amount of high fidelity design and failure information available on

the actual system under development. Applying the human ability to learn from experience and

augment our state of knowledge to evolve better solutions mitigates this limitation. However, the

challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally

human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets

of generalization, Bayesian inference, and probabilistic risk analysis.

We introduce an integrated framework for inferring the reliability, or other probabilistic

measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our

framework is based on learning from the performance of precedent designs and then applying the

acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process.

This dissertation presents a method for inferring properties of the conceptual variant using a

pseudo-spatial model that describes the spatial configuration of the family of systems to which the

concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial

model based on rank-ordered subjective expert perception of design similarity between systems

that elucidate the psychological space of the family. By a novel extension of Kriging methods for

analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a

Bayesian inference model that allows prediction of the probabilistic measure of interest.