We propose a new \textit{quadratic programming-based} method of approximating a nonstandard density using a multivariate Gaussian density. Such nonstandard densities usually arise while developing posterior samplers for unobserved components models involving inequality constraints on the parameters. For instance, Chan et al. (2016) provided a new model of trend inflation with linear inequality constraints on the stochastic trend. We implemented the proposed quadratic programming-based method for this model and compared it to the existing approximation. We observed that the proposed method works as well as the existing approximation in terms of the final trend estimates while achieving gains in terms of sample efficiency.

翻译：我们提出了一种新的基于二次规划的方法，利用多元高斯密度近似非标准密度。这类非标准密度通常出现在为涉及参数不等式约束的未观测成分模型开发后验抽样器时。例如，Chan等人（2016）提出了一种新的趋势通胀模型，其中对随机趋势施加了线性不等式约束。我们将所提出的基于二次规划的方法应用于该模型，并与现有近似方法进行了比较。我们观察到，所提方法在最终趋势估计方面与现有近似方法表现相当，同时在样本效率方面取得了提升。