论文标题
使用递归分区的黑盒密度函数估计
Black-box density function estimation using recursive partitioning
论文作者
论文摘要
我们提出了一种通过顺序决策循环定义的贝叶斯推论和一般贝叶斯计算的新方法。我们的方法定义了样品空间的递归分区。它既不依赖梯度，也不需要任何特定问题的调整，并且对于具有有界域的任何密度函数而言，渐近地是确切的。输出是通过在有效的数据结构中组织的分区对整个密度函数（包括归一化常数）的近似值。此类近似值可用于证据估计或快速后采样，也可以用作治疗较大类别估计问题的基础。该算法显示了有关合成和现实世界中最新方法的竞争性能，包括重力波物理学的参数推断。
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop. Our method defines a recursive partitioning of the sample space. It neither relies on gradients nor requires any problem-specific tuning, and is asymptotically exact for any density function with a bounded domain. The output is an approximation to the whole density function including the normalisation constant, via partitions organised in efficient data structures. Such approximations may be used for evidence estimation or fast posterior sampling, but also as building blocks to treat a larger class of estimation problems. The algorithm shows competitive performance to recent state-of-the-art methods on synthetic and real-world problems including parameter inference for gravitational-wave physics.
