Simulation-based inference with approximately correct parameters via maximum entropy
Simulation-based inference with approximately correct parameters via maximum entropy
复制标题
DOI：
10.1088/2632-2153/ac6286
复制
发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Rainier Barrett;Mehrad Ansari;Gourab Ghoshal;Andrew D. White
中科院分区：
文献类型：
--
作者：
Rainier Barrett;Mehrad Ansari;Gourab Ghoshal;Andrew D. White
从观察中推断模拟器的输入参数是从流行病学到分子动力学应用的一个关键挑战。在这里，我们展示了一种在稀疏数据和近似正确的模型的情况下的简单方法，当试图使用现有模型来推断观测数据的潜在变量时，这是很常见的。该方法基于最大熵原理(MaxEnt)，可以证明对潜在联合分布的改变最小，以适应新的数据。这种方法不需要似然或模型导数，其拟合对先验强度不敏感，消除了平衡观测数据拟合和先验信念的需要。该方法要求数据符合预期，这在某些设置中是正确的，并且在数据点较少的所有设置中可能是合理的。该方法是基于样本加权的，因此它的渐近运行时间与先验分布维度无关。我们演示了这种MaxEnt方法，并在三个系统上与其他无似然推理方法进行了比较：点粒子在引力场中运动，流行病传播的隔室模型和蛋白质的分子动力学模拟。
Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy (MaxEnt) and provably makes the smallest change in the latent joint distribution to fit new data. This method requires no likelihood or model derivatives and its fit is insensitive to prior strength, removing the need to balance observed data fit with prior belief. The method requires the ansatz that data is fit in expectation, which is true in some settings and may be reasonable in all settings with few data points. The method is based on sample reweighting, so its asymptotic run time is independent of prior distribution dimension. We demonstrate this MaxEnt approach and compare with other likelihood-free inference methods across three systems: a point particle moving in a gravitational field, a compartmental model of epidemic spread and molecular dynamics simulation of a protein.