Scientists worldwide are increasingly faced with the need to assess geologic hazards for very infrequent events that have high consequence, for instance, in siting nuclear facilities for volcanic hazards. One of the methods currently being developed for such assessments is the Bayesian method. This paper outlines the Bayesian technique by focusing on the volcanic hazard assessment for the Armenia Nuclear Power Plant, (ANPP), which is located in a Quaternary volcanic field. The Bayesian method presented in this paper relies on the development of a probabilistic model based on the spatial distribution of past volcanic events and a geologic process model. To develop the probabilistic model a bivariate Gaussian kernel function is used to forecast probabilities based on estimates of λt, temporal recurrence rate and λs, spatial density. Shortcomings often cited in such purely probabilistic assessments are that it takes into account only known features and the event, new volcano formation, is rare and there is no opportunity for repeated experiments or uniform observations, the hallmarks of classical probability. One approach to improving such probabilistic models is to incorporate related geological data that reflect controls on vent distribution and would improve the accuracy of subsequent models. Geophysical data indicate that volcanism in Armenia is closely linked to crustal movement along major right lateral strike-slip fault systems that generates transtension across region. The surface expression of this transtension is pull-apart basins, filled with thick deposits of sediment, and antithetic normal faults. Volcanism in Armenia is concentrated in these deep sedimentary basins as is reflected in regional gravity data surveys. This means that low gravity anomalies are likely good indicators of future volcanic activity and therefore would improve probabilistic hazard models. Therefore, gravity data are transformed into a likelihood function and combined with the original probability model in quantitative fashion using Bayesian statistics. The result is a model that is based on the distribution of past events but modified to include pertinent geologic information. Using the Bayesian approach in this example increases the uncertainty, or range in probability, which reflects how well we actually know our probability estimate. Therefore, we feel it is appropriate to consider a range in probabilities for volcanic disruption of the ANPP, 1-4 x 10-6 per year (t=1 yr). We note that these values exceed the current International Atomic Energy Agency standard, 1 x 10-7 per year by at least one order of magnitude.