Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials
Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials
复制标题
混合截断指数的混合贝叶斯网络中概率密度函数的近似
作者：
Barry R. Cobb;Prakash P. Shenoy;R. Rumí
截断指数（MTE）势的混合是一种替代离散化和蒙特卡罗方法求解混合贝叶斯网络。任何概率密度函数（PDF）都可以近似为MTE势，它总是可以以封闭形式边缘化。这使得传播可以完全使用Shenoy-Shafer架构来计算边缘，而对连接树的构造没有限制。本文提出了MTE潜力，近似标准PDF的和应用这些潜力解决推理问题的混合贝叶斯网络。这些近似将扩展可以用贝叶斯网络建模的推理问题的类型，如使用三个示例所示。
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.