The Application of Continuous-Time Markov Chain Models in the Analysis of Choice Flume Experiments
提出一种非齐次连续时间马尔可夫链模型，用于量化选择实验中动物的偏好或回避行为，并应用于评估海鲈对氯化海水的反应，克服了个体追踪和自相关等问题。
Abstract An inhomogeneous continuous-time Markov chain model is proposed to quantify animal preference and avoidance behaviour in a choice experiment. We develop and apply our model to a choice flume experiment designed to assess the preference or avoidance responses of sea bass (Dicentrarchus labrax) exposed to chlorinated seawater. Due to observed fluctuations in chlorine levels, a stochastic process was applied to describe and account for uncertainty in chlorine concentrations. A hierarchical model was implemented to account for differences between eight experimental runs and use Bayesian methods to quantify preference/avoidance after accounting for observed shoaling behaviour. The application of our method not only overcomes the need to track individuals during an experiment but also circumvents temporal autocorrelation and any violations of independence. Our model therefore surpasses current methods in choice chamber studies, incorporating variability in the environment and group-level dynamics to yield results that scale and generalise to the real-world.