We consider the nature and utility of marginal decompression sickness (DCS) events in fitting probabilistic decompression models to experimental dive trial data. Previous works have assigned various fractional weights to marginal DCS events, so that they contributed to probabilistic model parameter optimization, but less so than did full DCS events. Inclusion of fractional weight for marginal DCS events resulted in more conservative model predictions. We explore whether marginal DCS events are correlated with exposure to decompression or are randomly occurring events. Three null models are developed and compared with a known decompression model that is tuned on dive trial data containing only marginal DCS and non-DCS events. We further investigate the technique by which marginal DCS events were previously included in parameter optimization, explore the effects of fractional weighting of marginal DCS events on model optimization, and explore the rigor of combining data containing full and marginal DCS events for probabilistic DCS model optimization. We find that although marginal DCS events are related to exposure to decompression, empirical dive data containing marginal and full DCS events cannot be combined under a single DCS model. Furthermore, we find analytically that the optimal weight for a marginal DCS event is 0. Thus marginal DCS should be counted as no-DCS events when probabilistic DCS models are optimized with binomial likelihood functions. Specifically, our study finds that inclusion of marginal DCS events in model optimization to make the dive profiles more conservative is counterproductive and worsens the model's fit to the full DCS data.
Adaptive Speaker Recognition Based on Hidden Markov Model Parameter Optimization
