Large-sample asymptotics of the pseudo-marginal method
伪边际方法的大样本渐近理论AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究一般概率目标下的伪边际 MCMC 渐近理论与计算调节，未明确连接所列经济模型或应用方向。
反馈分类问题正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
伪边际方法的大样本渐近理论
- 研究问题
- 当目标分布的未归一化密度只能无偏估计时，伪边际 MCMC 如何平衡密度估计成本与遍历平均的渐近方差？
- 方法与思路
- 作者在正则条件下研究空间重缩放链的大样本弱收敛，再利用极限链分析正态随机游走提议与蒙特卡洛样本数。
- 材料陈述的贡献
- 摘要称极限链满足对数密度估计误差分布与参数位置无关的条件，并给出依赖参数维度的大样本调节指导。
- 阅读关注点
- 应核对无偏密度估计及正则条件、链的重缩放方式、优化所用效率准则，以及蒙特卡洛样本数的选择规则。
- 阅读边界
- 仅据所给 Crossref 摘要，无法给出具体调节数值；极限链性质也不能直接保证有限样本链满足相同条件。
原文摘要
Summary The pseudo-marginal algorithm is a variant of the Metropolis–Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate unbiasedly an unnormalized version of its density. Practically, one has to trade off the computational resources used to obtain this estimator against the asymptotic variances of the ergodic averages obtained by the pseudo-marginal algorithm. Recent works on optimizing this trade-off rely on some strong assumptions, which can cast doubts over their practical relevance. In particular, they all assume that the distribution of the difference between the log-density, and its estimate is independent of the parameter value at which it is evaluated. Under regularity conditions we show that as the number of data points tends to infinity, a space-rescaled version of the pseudo-marginal chain converges weakly to another pseudo-marginal chain for which this assumption indeed holds. A study of this limiting chain allows us to provide parameter dimension-dependent guidelines on how to optimally scale a normal random walk proposal, and the number of Monte Carlo samples for the pseudo-marginal method in the large-sample regime. These findings complement and validate currently available results.