论文标题
马尔可夫过渡率的自适应贝叶斯推断
Adaptive Bayesian Inference of Markov Transition Rates
论文作者
论文摘要
最佳设计最小化了准确估算模型参数所需的实验运行数量（样本），从而导致算法有效地最大程度地减少了参数估计值的方差。在对过去观察结果的知识下，自适应方法在线调整了采样约束，因为模型参数估计是完善的，不断最大化预期信息或减少方差。我们将自适应贝叶斯推断应用于马尔可夫链的估计过渡速率，马尔可夫链是一种自然界随机过程的常见模型。与大多数以前的研究不同，我们的顺序贝叶斯最佳设计将对每个观察结果进行更新，并且可以简单地扩展到两态模型到出生死亡过程和多态模型。通过迭代地找到获取每个样本的最佳时间，我们的自适应算法最大程度地降低了方差，从而在广泛的马尔可夫链参数化和构象中导致地面真相参数估计的总体误差降低。
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.
