标题： 系统生物学中伪边缘方法的计算推理实用指南 显示英文标题

标题： A practical guide to pseudo-marginal methods for computational inference in systems biology

摘要： 对于系统生物学中有兴趣的许多随机模型，例如描述生化反应网络的模型，通过统计推断精确量化参数不确定性是难以处理的。 当模型的似然函数难以计算但通过随机模拟正向问题可以生成大量样本路径时，无似然计算推理技术能够实现参数推理。 系统生物学中最常见的无似然方法是近似贝叶斯计算，它接受导致随机模拟与测量数据之间低差异的参数。 然而，很难评估接受阈值和差异函数的选择如何影响推断的准确性。 伪边缘方法是一种替代的无似然推理方法，它利用了似然函数的蒙特卡罗估计。 这种方法在噪声、部分观测的时间序列数据（生化反应网络研究中的典型情况）背景下具有多个优势。 具体来说，伪边缘方法能够实现精确推理和不确定性量化，并且可以与粒子滤波器高效结合以实现低方差、高精度的似然估计。 在本文综述中，我们使用生化反应网络推理作为案例研究，提供伪边缘方法的实际介绍。 使用Julia编程语言提供了关键算法的实现和示例；Julia是一种高性能、开源的科学计算编程语言。 显示英文摘要

摘要： For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable. Likelihood-free computational inference techniques enable parameter inference when the likelihood function for the model is intractable but the generation of many sample paths is feasible through stochastic simulation of the forward problem. The most common likelihood-free method in systems biology is approximate Bayesian computation that accepts parameters that result in low discrepancy between stochastic simulations and measured data. However, it can be difficult to assess how the accuracy of the resulting inferences are affected by the choice of acceptance threshold and discrepancy function. The pseudo-marginal approach is an alternative likelihood-free inference method that utilises a Monte Carlo estimate of the likelihood function. This approach has several advantages, particularly in the context of noisy, partially observed, time-course data typical in biochemical reaction network studies. Specifically, the pseudo-marginal approach facilitates exact inference and uncertainty quantification, and may be efficiently combined with particle filters for low variance, high-accuracy likelihood estimation. In this review, we provide a practical introduction to the pseudo-marginal approach using inference for biochemical reaction networks as a series of case studies. Implementations of key algorithms and examples are provided using the Julia programming language; a high performance, open source programming language for scientific computing.