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：我们建议采用新的 \ textit{ quadratic 编程基础} 方法,使用多变量高斯密度,以近似非标准密度。这种非标准密度通常产生,同时为未观察到的参数受不平等限制的部件模型开发后端取样器。例如,Chan 等人(2016年) 提供了一个新的趋势通货膨胀模式,对随机趋势施加线性不平等限制。我们为这一模型采用了拟议的四面式编程方法,并将其与现有的近似值进行了比较。我们发现,拟议的方法在取得抽样效率的同时,在最终趋势估计方面既发挥了作用,也取得了现有近似值。