Delayed-acceptance is a technique for reducing computational effort for Bayesian models with expensive likelihoods. Using a delayed-acceptance kernel for Markov chain Monte Carlo can reduce the number of expensive likelihoods evaluations required to approximate a posterior expectation. Delayed-acceptance uses a surrogate, or approximate, likelihood to avoid evaluation of the expensive likelihood when possible. Within the sequential Monte Carlo framework, we utilise the history of the sampler to adaptively tune the surrogate likelihood to yield better approximations of the expensive likelihood, and use a surrogate first annealing schedule to further increase computational efficiency. Moreover, we propose a framework for optimising computation time whilst avoiding particle degeneracy, which encapsulates existing strategies in the literature. Overall, we develop a novel algorithm for computationally efficient SMC with expensive likelihood functions. The method is applied to static Bayesian models, which we demonstrate on toy and real examples, code for which is available at https://github.com/bonStats/smcdar.

翻译：延迟接受是降低巴伊西亚模型计算能力的一种技术,其可能性非常昂贵。 使用马尔科夫链的延迟接受内核,蒙特卡洛可以减少为近似事后预期所需的昂贵可能性评估的数量。 延迟接受使用替代或近似可能性,以避免对昂贵可能性的评估。 在接连的蒙特卡洛框架内,我们利用取样员的历史来适应替代可能性,以得出更准确的昂贵可能性,并使用第一个代孕时间表来进一步提高计算效率。 此外,我们提议了一个框架,以优化计算时间,同时避免粒子衰减,同时在文献中包含现有战略。总的来说,我们为计算效率高可能性功能的SMC开发了新式算法。 这种方法适用于静态的巴伊斯模型,我们用玩具和真实例子来展示这些模型,其代码可在https://github.com/bonStats/scdar查阅 https://github.com/bonStat/smcdar查阅。