Increasingly complex datasets pose a number of challenges for Bayesian
inference. Conventional posterior sampling based on Markov chain Monte Carlo
can be too computationally intensive, is serial in nature and mixes poorly
between posterior modes. Further, all models are misspecified, which brings
into question the validity of the conventional Bayesian update. We present a
scalable Bayesian nonparametric learning routine that enables posterior
sampling through the optimization of suitably randomized objective functions. A
Dirichlet process prior on the unknown data distribution accounts for model
misspecification, and admits an embarrassingly parallel posterior bootstrap
algorithm that generates independent and exact samples from the nonparametric
posterior distribution. Our method is particularly adept at sampling from
multimodal posterior distributions via a random restart mechanism. We
demonstrate our method on Gaussian mixture model and sparse logistic regression
examples.

本文提出了一种可扩展的贝叶斯非参数学习方法，通过优化适当随机化的目标函数实现后验抽样，一个未知数据分布的狄利克雷过程先验考虑到了模型建立错误，并允许具有尴尬并行事后自举算法的非参数后验分布的独立和精确的样本生成，从而特别擅长从多峰后验分布中抽样。

使用后验自助法从多模式后验中进行可伸缩的非参数采样

Scalable Nonparametric Sampling from Multimodal Posteriors with the  Posterior Bootstrap

The name entity disambiguation task aims to partition the records of multiple
real-life persons so that each partition contains records pertaining to a
unique person. Most of the existing solutions for this task operate in a batch
mode, where all records to be disambiguated are initially available to the
algorithm. However, more realistic settings require that the name
disambiguation task be performed in an online fashion, in addition to, being
able to identify records of new ambiguous entities having no preexisting
records. In this work, we propose a Bayesian non-exhaustive classification
framework for solving online name disambiguation task. Our proposed method uses
a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model
which enables identification of new ambiguous entities who have no records in
the training data. For online classification, we use one sweep Gibbs sampler
which is very efficient and effective. As a case study we consider
bibliographic data in a temporal stream format and disambiguate authors by
partitioning their papers into homogeneous groups. Our experimental results
demonstrate that the proposed method is better than existing methods for
performing online name disambiguation task.

本文提出了一种基于贝叶斯非排它性分类框架的在线姓名消歧方法，使用 Dirichlet 过程先验和正常 * 正常 * Inverse Wishart 数据模型来识别训练数据中没有记录的新不确定实体，使用一次扫描 Gibbs 采样器进行在线分类，以期将作者的论文划分为同质群组以进行在线姓名消歧任务。结果表明，该方法优于现有方法。

基于贝叶斯的非完全分类算法 —— 在线姓名消歧案例研究

Bayesian Non-Exhaustive Classification A Case Study: Online Name  Disambiguation using Temporal Record Streams

Joint alignment of a collection of functions is the process of independently
transforming the functions so that they appear more similar to each other.
Typically, such unsupervised alignment algorithms fail when presented with
complex data sets arising from multiple modalities or make restrictive
assumptions about the form of the functions or transformations, limiting their
generality. We present a transformed Bayesian infinite mixture model that can
simultaneously align and cluster a data set. Our model and associated learning
scheme offer two key advantages: the optimal number of clusters is determined
in a data-driven fashion through the use of a Dirichlet process prior, and it
can accommodate any transformation function parameterized by a continuous
parameter vector. As a result, it is applicable to a wide range of data types,
and transformation functions. We present positive results on synthetic
two-dimensional data, on a set of one-dimensional curves, and on various image
data sets, showing large improvements over previous work. We discuss several
variations of the model and conclude with directions for future work.

该论文提出了一种基于贝叶斯无限混合模型的转换方法，该方法在不受函数或转换形式限制的情况下可以同时对数据集进行聚类和对齐，并通过使用狄利克雷过程先验来以数据驱动的方式确定聚类的最优数量，并且可适用于多种类型和转换函数的数据，该方法通过在合成数据和各种图像数据集上的实验取得了积极的结果。