Probabilistic Safety Assessment (PSA) tools typically assume independence between basic events. An event has the same probability irrespective of the accident scenario – a specific combination of events which leads to the investigated consequence. This brings obvious advantages for efficiecy of calculations and it is also easier to maintain the correctness of performed calculations when the event has the same set of reliability parameters and thereby probability in all combinations. This is also the case for most of the current Minimal Cutset list quantification algorithms. However, there are emerging needs for conditional quantification in situations where improved handling of dependencies could yield more realistic analysis results. These cases are exemplified by (1) dependencies between operator actions, (2) correlations between events in PSA, e.g., incurred by seismic events, and (3) common cause failure modeling. This work discusses quantification methods aiming for an implementation in the RiskSpectrum domain with the large PSA studies in focus.