两两Ising逆问题的快速解法
Faster solutions of the inverse pairwise Ising problem
摘要：
最近的研究表明，基于成对相互作用的概率模型--在最简单的情况下，伊辛模型--对从神经元到基因的真实生物网络的实验提供了令人惊讶的准确描述。找到这些模型需要我们解决一个逆问题：给定实验测量的期望值，底层哈密顿量的参数是什么？这个问题位于统计物理学和机器学习的交叉点，我们建议通过合并这两个领域的想法来实现更有效的解决方案。我们使用最近的坐标下降算法与直方图蒙特卡罗方法的适应相结合，并实现这些技术，以利用在真实神经元上的数据中发现的稀疏性。由此产生的算法在几分钟内学习描述40个神经元网络的伊辛模型的参数。这为分析现在出现的更大的数据集提供了可能性，从而测试了关于这些网络集体行为的假设。
英文摘要：
Recent work has shown that probabilistic models based on pairwise interactions-in the simplest case, the Ising model-provide surprisingly accurate descriptions of experiments on real biological networks ranging from neurons to genes. Finding these models requires us to solve an inverse problem: given experimentally measured expectation values, what are the parameters of the underlying Hamiltonian? This problem sits at the intersection of statistical physics and machine learning, and we suggest that more efficient solutions are possible by merging ideas from the two fields. We use a combination of recent coordinate descent algorithms with an adaptation of the histogram Monte Carlo method, and implement these techniques to take advantage of the sparseness found in data on real neurons. The resulting algorithm learns the parameters of an Ising model describing a network of forty neurons within a few minutes. This opens the possibility of analyzing much larger data sets now emerging, and thus testing hypotheses about the collective behaviors of these networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
