使用具有加性激发和乘法抑制的多变量Hawkes过程建模动物通信
Modeling Animal Communication Using Multivariate Hawkes Processes with Additive Excitation and Multiplicative Inhibition
AI总结：
本文提出了一种结合加性激发和乘法抑制的多变量Hawkes过程模型，用于分析动物声音通信中的时间依赖性及相互抑制效应。
摘要：
动物声音通信常常表现出时间依赖性，同一类型、个体或物种内的叫声会触发或抑制后续的叫声。尽管Hawkes过程为建模激发提供了自然框架，但在多变量设置中引入抑制可能会引发可识别性问题并复杂化参数解释。我们提出了一种灵活的多变量Hawkes过程类，结合了加性激发与乘法抑制。这种公式保留了激发的分支过程解释，同时减少了激发与抑制之间的干扰，并允许直接量化背景和激发对事件率的贡献。通过马尔可夫链蒙特卡洛进行贝叶斯推断，并利用随机时间变换定理评估模型充分性。所提出的方法通过模拟进行评估，并应用于两个声音通信数据集：群居的刺猬猴数据集，我们分析了三种具有不同行为作用的叫声类型，以及涉及座头鲸和北大西洋露脊鲸的两种物种数据集。刺猬猴分析显示存在显著的同类型和跨类型激发与跨类型抑制，而鲸类数据则主要显示同物种内的激发。
英文摘要：
Animal acoustic communication often exhibits temporal dependence, with calls triggering or suppressing subsequent calls within and across call types, individuals, or species. While Hawkes processes provide a natural framework for modeling excitation, incorporating inhibition in multivariate settings can raise identifiability issues and complicate parameter interpretation. We propose a flexible class of multivariate Hawkes processes that combines additive excitation with multiplicative inhibition. This formulation preserves the branching process interpretation of excitation while reducing confounding between excitation and inhibition, and allows direct quantification of background and excitation contributions to the event rate. Bayesian inference is conducted via Markov chain Monte Carlo, and model adequacy is assessed using the random time change theorem. The proposed methodology is evaluated through simulation and applied to two acoustic communication datasets: group-living meerkats, for which we analyze three selected call types with distinct behavioral roles, and a two-species baleen whale dataset involving humpback and North Atlantic right whales. The meerkat analysis reveals significant within- and cross-type excitation with cross-type inhibition, whereas the whale data show evidence primarily of within-species excitation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
