Adaptive Bayesian inference of Markov transition rates
Adaptive Bayesian inference of Markov transition rates
复制标题
马尔可夫转移率的自适应贝叶斯推理
DOI：
10.1098/rspa.2022.0453
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Kilpatrick, Zachary P.
中科院分区：
文献类型：
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作者：
Barendregt, Nicholas W.;Webb, Emily G.;Kilpatrick, Zachary P.
最优设计最大限度地减少了准确估计模型参数所需的实验运行（样本）数量，从而产生了有效地最小化参数估计方差的算法。受过去观测的知识支配，自适应方法在线调整采样约束，因为模型参数估计被细化，不断最大化获得的期望信息或方差减小。我们应用自适应贝叶斯推理来估计马尔可夫链的转移率，马尔可夫链是自然界中随机过程的一类常见模型。与大多数以前的研究不同，我们的序贯贝叶斯最优设计随着每次观察而更新，并且可以简单地扩展到两状态模型之外的生灭过程和多状态模型。通过迭代地找到获得每个样本的最佳时间，我们的自适应算法最大限度地降低了方差，从而降低了广泛的马尔可夫链参数化和构象的地面真值参数估计的总体误差。
Optimal designs minimize the number of experimental runs (samples) needed to accurately estimate model parameters, resulting in algorithms that, for instance, efficiently minimize parameter estimate variance. Governed by knowledge of past observations, adaptive approaches adjust sampling constraints online as model parameter estimates are refined, continually maximizing expected information gained or variance reduced. We apply adaptive Bayesian inference to estimate transition rates of Markov chains, a common class of models for stochastic processes in nature. Unlike most previous studies, our sequential Bayesian optimal design is updated with each observation and can be simply extended beyond two-state models to birth–death processes and multistate models. By iteratively finding the best time to obtain each sample, our adaptive algorithm maximally reduces variance, resulting in lower overall error in ground truth parameter estimates across a wide range of Markov chain parameterizations and conformations.