Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing environments are needed. Sequential Monte Carlo comes very close to this ideal whereas other approaches like Markov chain Monte Carlo do not. This paper presents a sequential posterior simulator well suited to this computing environment. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm is robust to pathological posterior distributions, generates accurate marginal likelihood approximations, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
University of Oxford（牛津大学）；CERN（欧洲核子研究中心）；Lawrence Berkeley National Lab（劳伦斯伯克利国家实验室）；University of British Columbia（英属哥伦比亚大学）；University of Liege（列日大学）；Intel Corporation（英特尔公司）；New York University（纽约大学）
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Informatics Institute（信息学研究所）；University of Amsterdam（阿姆斯特丹大学）；Donald Bren School of Information and Computer Sciences（唐纳德·布伦信息与计算机科学学院）；University of California, Irvine（加州大学欧文分校）；Canadian Institute for Advanced Research（加拿大高等研究院）
TU Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；Center of Advanced European Studies Research (caesar)（欧洲高级研究中心）；Helmholtz-Zentrum Geesthacht（盖斯特哈赫特亥姆霍兹研究中心）；University of Tübingen（蒂宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
