论文标题
从随机反应网络中确切的部分状态观察的状态和参数估计
State and parameter estimation from exact partial state observation in stochastic reaction networks
论文作者
论文摘要
我们考虑通过离散状态模拟的化学反应网络，并在时间马尔可夫过程中为物种的矢量拷贝数进行了连续，并根据对某些物种在连续的时间内的精确观察提供了一种新型的粒子滤波器方法和参数估计。表明未观察到的状态的条件概率分布满足具有跳跃的微分方程系统。我们提供了一种模拟一个过程的方法，该过程是代理未观察到的物种的矢量拷贝数。然后，使用产生的加权蒙特卡洛模拟来计算未观察到的物种的条件概率分布。我们还展示了如何将算法适应参数的贝叶斯估计以及基于直到将来时间的观察值的过去状态价值的估算。
We consider chemical reaction networks modeled by a discrete state and continuous in time Markov process for the vector copy number of the species and provide a novel particle filter method for state and parameter estimation based on exact observation of some of the species in continuous time. The conditional probability distribution of the unobserved states is shown to satisfy a system of differential equations with jumps. We provide a method of simulating a process that is a proxy for the vector copy number of the unobserved species along with a weight. The resulting weighted Monte Carlo simulation is then used to compute the conditional probability distribution of the unobserved species. We also show how our algorithm can be adapted for a Bayesian estimation of parameters and for the estimation of a past state value based on observations up to a future time.
