论文标题
汤普森抽样的部分可能性
Partial Likelihood Thompson Sampling
论文作者
论文摘要
我们考虑了决定如何最好地靶向和优先考虑现有疫苗的问题，这些疫苗可能可以保护对传染病的新变体的保护。顺序实验是一种有前途的方法。但是，由于反馈的延迟以及疾病流行的总体起伏和流动的挑战使得该任务不适用的方法可用。我们提出了一种可以应对这些挑战的方法，即汤普森采样。我们的方法涉及运行汤普森采样，每次观察事件时，都由部分可能性确定的信念更新。为了测试我们的方法，我们根据美国的Covid-19感染数据200天进行了半合成实验。
We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the overall ebb and flow of disease prevalence make available methods inapplicable for this task. We present a method, partial likelihood Thompson sampling, that can handle these challenges. Our method involves running Thompson sampling with belief updates determined by partial likelihood each time we observe an event. To test our approach, we ran a semi-synthetic experiment based on 200 days of COVID-19 infection data in the US.
