论文标题
加速顺序蒙特卡洛，具有替代可能性
Accelerating sequential Monte Carlo with surrogate likelihoods
论文作者
论文摘要
延迟感知能力是减少具有昂贵可能性的贝叶斯模型的计算工作的技术。在马尔可夫链上使用延迟的接受核可以减少近似后期期望所需的昂贵似然评估的数量。延迟的接受度使用替代或近似的可能性，以避免在可能的情况下评估昂贵的可能性。在顺序的蒙特卡洛框架内，我们利用采样器的历史记录来适应替代可能性，以产生昂贵可能性的更好近似值，并使用替代的首次退火时间表来进一步提高计算效率。此外，我们提出了一个框架，以优化计算时间，同时避免粒子退化，该框架封装了文献中的现有策略。总体而言，我们为具有昂贵的可能性功能的计算有效SMC开发了一种新型算法。该方法应用于静态贝叶斯模型，我们在玩具和真实示例中演示了该方法，该代码可在https://github.com/bonstats/smcdar上获得。
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.
