Bayesian Methods for Causal Discovery

我感兴趣的问题是使用机器学习来模拟观察数据、形成假设以及选择实验来评估假设的科学过程。由于实验涉及到与系统中的变量相互作用（干预），这将改变系统从刚被观察到的时候，这需要使用机器学习来建模分布的变化。对于理解具有大量变量、未知相互作用和大量可用数据的复杂系统来说，能够完成上述任务的方法是必要的。例如，最近的技术进步使大量关于基因表达的观测数据成为可能，同时也使进行实验的能力得以实现。由于大量的变量和嘈杂的数据，分析这些数据以了解每个变量对其他变量的影响是令人望而却步的，但是理解这一点可以对药物设计和个性化医疗等领域产生巨大的影响。机器学习

The problem I am interested in is using machine learning to simulate the scientific process ofseeing data, forming hypotheses, and choosing experiments to evaluate the hypotheses. Asexperiments involve interacting with variables in a system (interventions), which changes thesystem from when it is just being observed, this requires using machine learning to modelchanges in distribution. A method that can do the above is necessary to understand complexsystems with a large number of variables, unknown interactions, and for which lots of data isavailable. For example, recent advances in technology have made lots of observational dataavailable about gene expressions, as well as the ability to carry out experiments.Analysing this data to learn the effects each variable has on others is prohibitive due to thelarge number of variables and noisy data, however understanding this can have massive impacton areas such as drug design and personalised medicine.General area Machine Learning