Bayesian mode inference for discrete distributions in economics and finance
提出一种针对离散数据分布的众数推断技术，通过拟合新型移位泊松分布混合来实现，并在贷款违约风险和通胀预期应用中验证其可信度和实用性。
We propose a straightforward technique for mode inference in discrete data distributions which involves fitting a mixture of novel shifted-Poisson distributions. The credibility and utility of our approach is demonstrated through applications pertaining to loan default risk and inflation expectations.