英文标题：
《Bayesian optimisation for fast approximate inference in state-space
  models with intractable likelihoods》
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作者：
Johan Dahlin, Mattias Villani and Thomas B. Sch\\\"on
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最新提交年份：
2017
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英文摘要：
  We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian computations (SMC-ABC) is one approach to approximate the likelihood in this type of models. However, such approximations can be noisy and computationally costly which hinders efficient implementations using standard methods based on optimisation and Monte Carlo methods. We propose a computationally efficient novel method based on the combination of Gaussian process optimisation and SMC-ABC to create a Laplace approximation of the intractable posterior. We exemplify the proposed algorithm for inference in stochastic volatility models with both synthetic and real-world data as well as for estimating the Value-at-Risk for two portfolios using a copula model. We document speed-ups of between one and two orders of magnitude compared to state-of-the-art algorithms for posterior inference.
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中文摘要：
我们考虑具有难处理似然性的非线性状态空间模型中的近似贝叶斯参数推断问题。序贯蒙特卡罗近似贝叶斯计算（SMC-ABC）是一种在这类模型中近似概率的方法。然而，这种近似可能会有噪声，计算成本高，这阻碍了使用基于优化和蒙特卡罗方法的标准方法的有效实现。我们提出了一种基于高斯过程优化和SMC-ABC相结合的计算效率高的新方法，以创建难以处理的后验概率的拉普拉斯近似。我们举例说明了在随机波动率模型中使用合成数据和真实数据进行推理的算法，以及使用copula模型估计两个投资组合的风险价值的算法。与最先进的后验推理算法相比，我们记录了一到两个数量级的速度提升。
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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