论文标题
具有非多项式分配的概率循环的基于力矩的不变性
Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments
论文作者
论文摘要
我们提出了一种方法，可以自动近似基于力矩的概率程序的不变式，并具有连续状态变量的非物理更新以适应更复杂的动力学。我们的方法利用多项式混沌扩展将近似于非线性功能更新作为正交多项式的总和。我们利用此结果自动估计具有非多功能更新的可溶解循环中所有订单的状态变量时刻。我们在几个示例中展示了估算方法的准确性，例如货币政策中的车辆模型和泰勒规则。
We present a method to automatically approximate moment-based invariants of probabilistic programs with non-polynomial updates of continuous state variables to accommodate more complex dynamics. Our approach leverages polynomial chaos expansion to approximate non-linear functional updates as sums of orthogonal polynomials. We exploit this result to automatically estimate state-variable moments of all orders in Prob-solvable loops with non-polynomial updates. We showcase the accuracy of our estimation approach in several examples, such as the turning vehicle model and the Taylor rule in monetary policy.
