We present a unified approach for learning the parameters of Sum-Product networks (SPNs). We prove that any complete and decomposable SPN is equivalent to a mixture of trees where each tree corresponds to a product of univariate distributions. Based on the mixture model perspective, we characterize the objective function when learning SPNs based on the maximum likelihood estimation (MLE) princi...

Geometric Programming (GP) is a class of nonlinear optimization withmany useful theoretical and computational properties. Over the last fewyears, GP has been used to solve a variety of problems in the analysisand design of communication systems in several ‘layers’ in the commu-nication network architecture, including information theory problems,signal processing algorithms, ...