Bayesian inference has been used to determine rigorous estimates of hydroxyl radical concentrations ([OH]) and air mass dilution rates (K) averaged following air masses between linked observations of nonmethane hydrocarbons (NMHCs) spanning the North Atlantic during the Intercontinental Transport and Chemical Transformation (ITCT)-Lagrangian-2K4 experiment. The Bayesian technique obtains a refined (posterior) distribution of a parameter given data related to the parameter through a model and prior beliefs about the parameter distribution. Here, the model describes hydrocarbon loss through OH reaction and mixing with a background concentration at rate K. The Lagrangian experiment provides direct observations of hydrocarbons at two time points, removing assumptions regarding composition or sources upstream of a single observation. The estimates are sharpened by using many hydrocarbons with different reactivities and accounting for their variability and measurement uncertainty. A novel technique is used to construct prior background distributions of many species, described by variation of a single parameter α. This exploits the high correlation of species, related by the first principal component of many NMHC samples. The Bayesian method obtains posterior estimates of [OH], K and α following each air mass. Median [OH] values are typically between 0.5 and 2.0 × 10⁶ molecules cm^-3, but are elevated to between 2.5 and 3.5 × 10⁶ molecules cm^-3, in low-level pollution. A comparison of [OH] estimates from absolute NMHC concentrations and NMHC ratios assuming zero background (the "photochemical clock" method) shows similar distributions but reveals systematic high bias in the estimates from ratios. Estimates of K are ∼0.1 day^-1 but show more sensitivity to the prior distribution assumed. Copyright 2007 by the American Geophysical Union.