论文标题
准Newton顺序蒙特卡洛
Quasi-Newton Sequential Monte Carlo
论文作者
论文摘要
连续蒙特卡洛采样器代表了贝叶斯模型中后推断的令人信服的方法，因为是可行的，并且提供了对后验正常常数的无偏估计。在这项工作中，我们通过采用L-BFGS Hessian近似值来显着加速蒙特卡洛采样器，该l-bfgs Hessian近似代表了全批优化技术的最新技术。 L-BFGS Hessian近似在参数维度中仅具有线性复杂性，并且不需要额外的后或梯度评估。所得的顺序蒙特卡洛算法是自适应，可行的，并且非常适合高维和多模式设置，我们在数值实验中证明了这方面有关挑战性后验分布的实验。
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian models, due to being parallelisable and providing an unbiased estimate of the posterior normalising constant. In this work, we significantly accelerate sequential Monte Carlo samplers by adopting the L-BFGS Hessian approximation which represents the state-of-the-art in full-batch optimisation techniques. The L-BFGS Hessian approximation has only linear complexity in the parameter dimension and requires no additional posterior or gradient evaluations. The resulting sequential Monte Carlo algorithm is adaptive, parallelisable and well-suited to high-dimensional and multi-modal settings, which we demonstrate in numerical experiments on challenging posterior distributions.
