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用Velocity Verlet积分器改进HMC抽样方法OA北大核心CSTPCD

Improving the HMC sampling method by Velocity Verlet integrator

中文摘要英文摘要

Hamilton Monte Carlo(HMC)方法是一种常用的快速抽样方法.在对哈密顿方程进行抽样时,HMC方法使用Leapfrog积分器,这可能造成方程的位置及动量的迭代值在时间上不同步,其产生的误差会降低抽样效率及抽样结果的稳定性.为此,本文提出了 IHMC(Im-proved HMC)方法,该方法用Velocity Verlet积分器替代Leapfrog积分器,每次迭代时都计算两变量在同一时刻的值.为验证方法的效果,本文进行了两个实验,一个是将该方法应用于非对称随机波动率模型(RASV模型)的参数估计,另一个是将方法应用于方差伽马分布的抽样,结果显示:IHMC方法比HMC方法的效率更高、结果更稳定.

Hamilton Monte Carlo(HMC)method is a fast sampling method.To sampling a Hamiltonian equation,the HMC method uses the Leapfrog integrator.As a result,unsynchronized values of position and momentum variables of the equation are possibly generated during the iterative process and resulting estimation error can seriously damage the sampling efficiency and stability of sampling result.To solve this problem,here we propose an improved HMC(IHMC)method by replacing the Leapfrog integrator in HMC method with Velocity Verlet integrator.The IHMC method calculates the variables simultane-ously in each iteration.Two numerical examples are implemented to check the performance of the IHMC method,one is the problem of estimating the parameters of realized and stochastic volatility(RASV)model with asymmetric effects,the other is the problem of sampling the variance Gamma distribution.It is shown that the IHMC method has higher sampling efficiency and more stable sampling result com-pared with the HMC method.

李婉荧;唐亚勇

四川大学数学学院,成都 610064

数学

HMC方法Velocity Verlet积分器RASV模型方差伽马分布

Hamiltonian Monte Carlo methodVelocity Verlet integratorRASV modelVariance Gam-ma distribution

《四川大学学报(自然科学版)》 2024 (001)

25-34 / 10

10.19907/j.0490-6756.2024.011004

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