论文标题
贝叶斯间接推断具有棘手归一化功能的模型
Bayesian Indirect Inference for Models with Intractable Normalizing Functions
论文作者
论文摘要
双重顽固性分布的推断很具有挑战性，因为这些模型的棘手的归一化功能包括感兴趣的参数。对于具有较大数据集的多维模型，以前的辅助变量MCMC算法是不可行的，因为它们依赖于每次迭代时昂贵的辅助变量仿真。我们通过用替代模型的计算廉价仿真替换昂贵的辅助变量模拟来开发快速的贝叶斯间接算法。我们使用高斯进程近似值学习替代模型参数与概率模型参数之间的关系。我们将我们的方法应用于挑战模拟和真实的数据示例，并说明该算法是否解决了双重棘手的分布的计算和推论挑战。特别是对于具有10个参数的大型社交网络模型，我们表明我们的方法可以将计算时间从大约2周减少到5小时，而不是先前的方法。我们的方法使从业者可以对比以前更大的数据集进行更复杂的模型进行贝叶斯推断。
Inference for doubly intractable distributions is challenging because the intractable normalizing functions of these models include parameters of interest. Previous auxiliary variable MCMC algorithms are infeasible for multi-dimensional models with large data sets because they depend on expensive auxiliary variable simulation at each iteration. We develop a fast Bayesian indirect algorithm by replacing an expensive auxiliary variable simulation from a probability model with a computationally cheap simulation from a surrogate model. We learn the relationship between the surrogate model parameters and the probability model parameters using Gaussian process approximations. We apply our methods to challenging simulated and real data examples, and illustrate that the algorithm addresses both computational and inferential challenges for doubly intractable distributions. Especially for a large social network model with 10 parameters, we show that our method can reduce computing time from about 2 weeks to 5 hours, compared to the previous method. Our method allows practitioners to carry out Bayesian inference for more complex models with larger data sets than before.
