The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques. Successful applications can be obtained using models inspired by the Boltzmann machine (BM) architecture. In this manuscript, the product Jacobi-Theta Boltzmann machine (pJTBM) is introduced as a restricted version of the Riemann-Theta Boltzmann machine (RTBM) with diagonal hidden sector connection matrix. We show that score matching, based on the Fisher divergence, can be used to fit probability densities with the pJTBM more efficiently than with the original RTBM.

翻译：概率密度函数的估计是一项非平凡的任务，近年来已借助机器学习技术得以处理。使用受玻尔兹曼机（BM）架构启发的模型，可以取得成功的应用。本文介绍了乘积雅可比-Theta玻尔兹曼机（pJTBM），作为黎曼-Theta玻尔兹曼机（RTBM）的一种受限版本，其隐藏层连接矩阵为对角形式。我们证明，基于Fisher散度的分数匹配方法可以比原始RTBM更高效地利用pJTBM拟合概率密度。