Manifold Stochastic Dynamics for Bayesian Learning
Manifold Stochastic Dynamics for Bayesian Learning
复制标题
贝叶斯学习的流形随机动力学
DOI：
10.1162/089976601753196021
复制
发表时间：
1999
影响因子：
2.9
通讯作者：
Y. Baram
中科院分区：
文献类型：
--
作者：
M. Zlochin;Y. Baram
我们提出了一种新的马尔可夫链蒙特卡罗算法，它是随机动力学方法的推广。该算法利用其内在的几何结构对状态空间进行探索，这有助于对复杂分布进行有效采样。应用于神经网络中的贝叶斯学习时，我们发现我们的算法可以产生与最先进的方法相当的结果，同时消耗的时间要少得多。
We propose a new Markov Chain Monte Carlo algorithm, which is a generalization of the stochastic dynamics method. The algorithm performs exploration of the state-space using its intrinsic geometric structure, which facilitates efficient sampling of complex distributions. Applied to Bayesian learning in neural networks, our algorithm was found to produce results comparable to the best state-of-the-art method while consuming considerably less time.