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On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
Ye He · Krishnakumar Balasubramanian · Murat Erdogdu

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1801

The randomized midpoint method, proposed by (Shen and Lee, 2019), has emerged as an optimal discretization procedure for simulating the continuous time underdamped Langevin diffusion. In this paper, we analyze several probabilistic properties of the randomized midpoint discretization method, considering both overdamped and underdamped Langevin dynamics. We first characterize the stationary distribution of the discrete chain obtained with constant step-size discretization and show that it is biased away from the target distribution. Notably, the step-size needs to go to zero to obtain asymptotic unbiasedness. Next, we establish the asymptotic normality of numerical integration using the randomized midpoint method and highlight the relative advantages and disadvantages over other discretizations. Our results collectively provide several insights into the behavior of the randomized midpoint discretization method, including obtaining confidence intervals for numerical integrations.

Author Information

Ye He (University of California, Davis)
Krishnakumar Balasubramanian (University of California, Davis)
Murat Erdogdu (University of Toronto)

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