We develop a Bayesian methodology for systemic risk assessment in financial networks such as the
interbank market. Nodes represent participants in the network and weighted directed edges represent
liabilities. Often, for every participant, only the total liabilities and total assets within this network are
observable. However, systemic risk assessment needs the individual liabilities. We propose a model
for the individual liabilities, which, following a Bayesian approach, we then condition on the observed
total liabilities and assets and, potentially, on certain observed individual liabilities. We construct a
Gibbs sampler to generate samples from this conditional distribution. These samples can be used in
stress testing, giving probabilities for the outcomes of interest. As one application we derive default
probabilities of individual banks and discuss their sensitivity with respect to prior information included
to model the network. An R-package implementing the methodology is provided.