论文标题
惩罚T-Walk MCMC
Penalised t-walk MCMC
论文作者
论文摘要
处理复杂的统计模型所产生的多模式处理仍然是一个挑战。当前的马尔可夫链蒙特卡洛（MCMC）方法可以解决此主题，这是基于针对电动分布产物的链组合的。尽管这种方法具有理论上的有效性，但由于涉及的高庞大成本，实际实现通常会遭受不良混合和缓慢的收敛性。在这项工作中，我们研究了T-Walk算法的新型扩展，这是一种现有的MCMC方法，它是廉价且不变的状态空间变换，用于处理多模式分布。我们承认，新方法的有效性将取决于问题，并且可能在复杂的情况下挣扎。对于这种情况，我们提出了一种基于伪划分理论的后处理技术，用于结合孤立的样品。
Handling multimodality that commonly arises from complicated statistical models remains a challenge. Current Markov chain Monte Carlo (MCMC) methodology tackling this subject is based on an ensemble of chains targeting a product of power-tempered distributions. Despite the theoretical validity of such methods, practical implementations typically suffer from bad mixing and slow convergence due to the high-computation cost involved. In this work we study novel extensions of the t-walk algorithm, an existing MCMC method that is inexpensive and invariant to affine transformations of the state space, for dealing with multimodal distributions. We acknowledge that the effectiveness of the new method will be problem dependent and might struggle in complex scenarios; for such cases we propose a post-processing technique based on pseudo-marginal theory for combining isolated samples.
