Nonparametric Bayesian estimation subject to overidentified moment equations is a challenge because the support of the posterior is a manifold of lower dimension than the number of model parameters. The manifold therefore has Lebesgue measure zero thus inhibiting the use of the most commonly used Bayesian estimation method: MCMC (Markov Chain Monte Carlo). This study proposes an effective MCMC algorithm and algorithms for estimating scale and the normalizing constant. The algorithms are illustrated with two illustrative applications.

中文翻译：

---

受过度识别矩条件影响的非参数贝叶斯

受过度识别矩方程影响的非参数贝叶斯估计是一个挑战，因为后验的支持是一个比模型参数数量低维的流形。因此，流形的 Lebesgue 测度为零，因此禁止使用最常用的贝叶斯估计方法：MCMC（马尔可夫链蒙特卡罗）。本研究提出了一种有效的 MCMC 算法和用于估计尺度和归一化常数的算法。这些算法用两个说明性应用来说明。