Decompression sickness (DCS) is a condition associated with reductions in ambient pressure during underwater diving and altitude exposure. Determining the risk of DCS from a dive exposure remains an active area of research, with the goal of developing safe decompression schedules to mitigate the occurrence of DCS. This work develops a probabilistic model for the trinomial outcome of full, marginal, and no DCS. The model treats full DCS and marginal DCS as separate, fully weighted hierarchical events. Six variants of exponential-exponential (EE) and linear-exponential (LE) decompression models were optimized to fit dive outcomes from the BIG292 empirical human dive trial data of 3322 exposures. Using the log likelihood difference test, the LE1 trinomial marginal model was determined to provide the best fit for the data. The LE1 trinomial marginal model can be used to better understand decompression schedules, expanding upon binomial models which treat marginal DCS as either a fractionally weighted event or a non-event. Future work could investigate whether the use of marginal DCS cases improves multinomial probabilistic DCS model performance. Model improvement could include the addition of a fourth outcome, where full DCS is split and categorized as serious or mild DCS, creating a tetranomial model with serious, mild, marginal, and no DCS outcomes for comparison with the presently developed model.