Ecologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal's behaviours, from an observed biologging time series. Modern technological devices such as video recorders and drones now allow researchers to directly observe an animal's behaviour. Using these observations as labels of the latent process can improve a hidden Markov model's accuracy when decoding the latent process. However, many wild animals are observed infrequently. Including such rare labels often has a negligible influence on parameter estimates, which in turn does not meaningfully improve the accuracy of the decoded latent process. We introduce a weighted likelihood approach that increases the relative influence of labelled observations. We use this approach to develop two hidden Markov models to decode the foraging behaviour of killer whales (Orcinus orca) off the coast of British Columbia, Canada. Using cross-validated evaluation metrics, we show that our weighted likelihood approach produces more accurate and understandable decoded latent processes compared to existing methods. Thus, our method effectively leverages sparse labels to enhance researchers' ability to accurately decode hidden processes across various fields.

翻译：生态学家常使用隐马尔可夫模型从观测到的生物记录时间序列中解码潜在过程，例如动物行为序列。现代技术设备（如摄像机和无人机）使研究者能够直接观测动物行为。将这些观测作为潜在过程的标签，可在解码潜在过程时提升隐马尔可夫模型的准确性。然而，许多野生动物被观测到的频率较低。纳入此类稀疏标签对参数估计的影响通常微乎其微，从而无法显著提升解码潜在过程的准确性。我们引入了一种加权似然方法，以增强已标记观测值的相对影响力。基于此方法，我们构建了两个隐马尔可夫模型，用于解码加拿大不列颠哥伦比亚省沿岸虎鲸（Orcinus orca）的觅食行为。通过交叉验证评估指标，我们证明相较于现有方法，我们的加权似然方法能够产生更准确且更易理解的解码潜在过程。因此，本方法能有效利用稀疏标签，增强研究者在不同领域准确解码隐藏过程的能力。