matLeap：一种适用于贝叶斯推断的快速自适应Matlab实现
matLeap: A fast adaptive Matlab-ready tau-leaping implementation suitable for Bayesian inference
摘要：
背景：化学反应网络模型中的物种丰度分布通常不能解析计算。相反，随机模拟算法允许从系统配置中采样。虽然已经描述了许多算法，但是没有为τ-跳跃提供快速实现，其中i）是Matlab兼容的，ii）在SSA、隐式和显式τ-跳跃之间自适应地交替，以及iii）提供贝叶斯推断所需的汇总统计。结果如下：我们提供了一个Matlab兼容的自适应显式-隐式τ-跳跃算法的实现，以解决上述缺陷。与两个广泛使用的仿真软件包相比，matLeap提供了相同或更快的结果，同时保持了准确性。最后，matLeap产生的统计量的随机过程不可用其他方法，这是必不可少的贝叶斯推断。结论：matLeap解决了现有Matlab兼容的随机模拟软件中的缺点，提供了显著的加速和求和统计，对于利用基于粒子滤波的方法进行贝叶斯推断的研究人员特别有用。代码可在https://github.com/claassengroup/matLeap下载。联系人：justin. imsb.biol.ethz.ch
英文摘要：
Background: Species abundance distributions in chemical reaction network models cannot usually be computed analytically. Instead, stochas- tic simulation algorithms allow sample from the the system configuration. Although many algorithms have been described, no fast implementation has been provided for τ-leaping which i) is Matlab-compatible, ii) adap- tively alternates between SSA, implicit and explicit τ-leaping, and iii) provides summary statistics necessary for Bayesian inference. Results: We provide a Matlab-compatible implementation of the adap- tive explicit-implicit τ-leaping algorithm to address the above-mentioned deficits. matLeap provides equal or substantially faster results compared to two widely used simulation packages while maintaining accuracy. Lastly, matLeap yields summary statistics of the stochastic process unavailable with other methods, which are indispensable for Bayesian inference. Conclusions: matLeap addresses shortcomings in existing Matlab-compatible stochastic simulation software, providing significant speedups and sum- mary statistics that are especially useful for researchers utilizing particle- filter based methods for Bayesian inference. Code is available for download at https://github.com/claassengroup/matLeap. Contact: justin.feigelman@imsb.biol.ethz.ch
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