Implementation of Sequential Importance Sampling in GPGPU
K Hayashi, Masaya Masayoshi Saito, R Yoshida, Tomoyuki Higuchi · 2010
The estimation of many unknown parameters is carried out using a simplified Sequential Importance Sampling (SIS) algorithm which is implemented in a graphic processing unit (GPU). The aim of the present work is to show technical points to bring out the performance of GPU. Using the implemented code, two numerical experiments are demonstrated. In the first demonstration, it is shown that a parameter estimation involving 109Monte Carlo samples is completed within eight hours. In the second demonstration, accuracy-guaranteed evaluation of the likelihood is carried out.