伪边际MCMC中的信息-计算倒置
Information-Computation Inversion in Pseudo-Marginal MCMC
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中文总结 AI 辅助
该研究针对伪边际MCMC，推导逆权重接受界确立信息-计算倒置，提出路线条件转移方法，经实验验证其可减少粒子滤波器运行时间并支持机制可移植性。
中文摘要 AI 辅助
伪边际MCMC在非负无偏似然估计下是精确的，但观测设计可同时改变后验信息和似然估计量的随机规律。我们研究在这种联合变化下，声明的后验泛函的固定时域恢复。针对一类广泛的新估计量伪边际方法，我们推导了逆权重接受界，该界可从差异状态中保留的高权重质量得出泛函均方误差的下界。一个双区域推论确立了信息-计算倒置：当估计量规律变化时，更强的精确后验分离可能与更差的有限时域恢复共存。这种阻碍促使我们提出一种精确的路线条件转移，该转移根据科学对路线分配耦合辅助继承和独立刷新。在受控转录实验中，更精细的观测增强了似然分离，同时降低了粒子滤波器的可靠性和有限链行为。在一个新的96状态基因网络比较中，路线条件在冻结执行中将测得的粒子滤波器挂钟时间减少了42.4%（95%保留状态自举区间为39.6%-45.2%）。恢复不确定性跨越预先声明的非劣效边际。一项前瞻性Lotka-Volterra实验支持保留状态路线恢复机制的可移植性。这些结果区分了伪边际推理中的统计信息、保留状态泛函风险和辅助计算分配。
英文摘要
Observation refinement changes posterior uncertainty and the likelihood calculation in pseudo-marginal MCMC. We compare their combined effect through finite-run squared-error risk. A sufficient inversion condition relates information gain to accepted event flow and coarse-kernel contraction. A bootstrap construction realizes inversion at every fixed particle count. We then couple cross-event proposals and refresh same-event proposals independently. A swap identity establishes invariance; continuation identities describe subsequent risk. In the same finite model, a rational certificate proves inversion against optimized constant mixtures and repair by selective allocation over an initialization class at a common action-price budget. Reaction-network experiments measure CPU costs. Under finite-pool initialization, selective allocation reduces event mean-squared error by 40.5% against a tuned mixture at 25 post-initialization CPU seconds. Paired transcription observations show how increased particle effort can raise finite-budget error.
发表机构
- School of Mathematics and Statistics, Qingdao University（青岛大学数学与统计学院）
机构由 AI 辅助整理，请以论文原文为准。
