A simple method for rejection sampling efficiency improvement on SIMT architectures

Ridley, Gavin; Forget, Benoit

doi:10.1007/s11222-021-10003-z

Cited by 5 publications

(1 citation statement)

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“…The typical methods of this kind of idea are the rejection sampling and the importance sampling. For the rejection sampling [17], although the sampling is from the proposal distribution, the sampled data need to be accepted with a certain probability according to the target distribution. So it is not necessary to pay attention to the CDF or the inverse CDF, but only require that the probability density of the target distribution can be calculated.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation

et al. 2023

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Random variate generation has been widely used in various engineering applications and model frameworks. The introduction of the proposal distribution makes this kind of methods can deal with more general sampling problems with complex probability density representation. However, the design of proposal distribution is usually difficult, especially when the target distribution is so complex that it contains asymmetric, multimodal and skewed, or even unsmooth, heavy tailed and illegal density, the sampling performance and efficiency will be greatly affected. In this paper, a novel parameter optimization method with efficient self-tuning strategy is proposed in order to automatically construct the optimal proposal distribution for any complex target density, which treats the design of proposal distribution as a parameter search process and search the optimal solution in an infeasible region by evaluating the loss of solution. The significant advantage of the proposed method is that the search based on infeasible region can converge to a good solution with only a few iterations, making our method far superior to other existing methods in efficiency, which is very suitable for the complex target distribution model with time-consuming density calculation process. Experimental results show the advantages of the proposed method in terms of search strategy, optimal solution performance, search efficiency and robustness.INDEX TERMS Monte Carlo methods, generative model, parameter optimization, rejection sampling, machine learning.

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Section: Introductionmentioning

confidence: 99%