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CONTROLLED SEQUENTIAL MONTE CARLO
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DOI：10.1214/19-AOS1914.png)
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Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and related fields; for example, for inference in nonlinear non-Gaussian state space models, and in complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which crucially impact their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. This method builds upon a number of existing algorithms in econometrics, physics and statistics for inference in state space models, and generalizes these methods so as to accommodate complex static models. We provide a theoretical analysis concerning the fluctuation and stability of this methodology that also provides insight into the properties of related algorithms. We demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications.
Keyword:
State space models
annealed importance sampling
normalizing constants
optimal control
approximate dynamic programming
reinforcement learning
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
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分享收藏Central limit theorem for sequential Monte Carlo methods and its application to bayesian inference序贯蒙特卡罗方法的中心极限定理及其在贝叶斯推理中的应用.png)
ANNALS OF STATISTICS
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ANNALS OF STATISTICSIF3.7
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