Accelerating sequential Monte Carlo with surrogate likelihoods
Accelerating sequential Monte Carlo with surrogate likelihoods
复制标题
使用替代可能性加速顺序蒙特卡罗
DOI：
10.1007/s11222-021-10036-4
复制
发表时间：
2020
影响因子：
2.2
通讯作者：
C. Drovandi
中科院分区：
文献类型：
--
作者：
Joshua J. Bon;Anthony Lee;C. Drovandi
延迟接受是一种用于减少具有昂贵似然性的贝叶斯模型的计算工作量的技术。使用马尔可夫链蒙特卡罗的延迟接受核可以减少近似后验期望所需的昂贵的似然估计的数量。延迟接受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。在顺序蒙特卡罗框架内，我们利用采样器的历史来自适应地调整代理似然性，以产生更好的近似昂贵的似然性，并使用代理第一退火时间表，以进一步提高计算效率。此外，我们提出了一个框架，优化计算时间，同时避免粒子退化，封装现有的策略在文献中。总的来说，我们开发了一种新的算法，计算效率高的SMC昂贵的似然函数。该方法适用于静态贝叶斯模型，我们证明玩具和真实的例子。
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.
影响因子：
5.8
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Rob
通讯作者：
Tibshirani, Rob
影响因子：
1.8
作者：
Beskos, Alexandros;Jasra, Ajay;Thiery, Alexandre
通讯作者：
Thiery, Alexandre