Bayesian Methods for Causal Discovery

我感兴趣的问题是使用机器学习来模拟科学过程，即看到数据，形成假设，并选择实验来评估假设。由于实验涉及与系统中的变量进行交互（干预），从而改变了刚刚观察到的分布，因此需要使用机器学习来模拟分布的变化。一种方法，可以做到以上是必要的，以了解复杂的系统，大量的变量，未知的相互作用，并为其中大量的数据是可用的。例如，最近的技术进步已经提供了大量关于基因表达的观测数据，以及进行实验的能力。由于大量的变量和噪声数据，分析这些数据以了解每个变量对其他变量的影响是禁止的，但是理解这些数据可以对药物设计和个性化医疗等领域产生巨大影响。一般领域机器学习

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