蚂蚁相互作用的贝叶斯惩罚隐马尔可夫模型
A Bayesian Penalized Hidden Markov Model for Ant Interactions
摘要：
社会动物之间的相互作用提供了对营养物质，疾病和社会接触的交换和流动的见解。我们认为，木蚁（\textit{Camponotus pennsylvanicus}）之间的trophaltaxis的相互作用超过4小时的每秒的观察室水平的分析。数据显示了快速和缓慢模式之间的明显切换。然而，拟合标准隐马尔可夫模型（HMM）会导致估计的隐藏状态过程过拟合此高分辨率数据，因为状态过程的波动速度比生物学上合理的要快一个数量级。我们提出了一种新的方法，通过贝叶斯岭之前的状态转换率，同时也将生物动机的协变量的惩罚估计的障碍。这种惩罚诱导平滑，限制状态切换的速率，结合群体内的适当协变量，以确保更生物可行的结果。我们开发了一个马尔可夫链蒙特卡罗算法进行贝叶斯推断的基础上离散化的接触网络的观察。
英文摘要：
Interactions between social animals provide insights into the exchange and flow of nutrients, disease, and social contacts. We consider a chamber level analysis of trophallaxis interactions between carpenter ants (\textit{Camponotus pennsylvanicus}) over 4 hours of second-by-second observations. The data show clear switches between fast and slow modes of trophallaxis. However, fitting a standard hidden Markov model (HMM) results in an estimated hidden state process that is overfit to this high resolution data, as the state process fluctuates an order of magnitude more quickly than is biologically reasonable. We propose a novel approach for penalized estimation of HMMs through a Bayesian ridge prior on the state transition rates while also incorporating biologically motivated covariates. This penalty induces smoothing, limiting the rate of state switching that combines with appropriate covariates within the colony to ensure more biologically feasible results. We develop a Markov chain Monte Carlo algorithm to perform Bayesian inference based on discretized observations of the contact network.
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