这个代码是policy iteration算法关于强化学习的. 请您用winzip 解压缩
标签： iteration policy winzip 代码
上传时间： 2015-04-24
上传用户：lepoke
computing singular value of matrix by iteration
标签： computing iteration singular matrix
上传时间： 2014-01-16
上传用户：lx9076
Based on Matlab,Gauss iteration Method
标签： iteration Matlab Method Based
上传时间： 2016-02-03
上传用户：cjf0304
matlab function ---> find roots using fixed-point iteration
标签： fixed-point iteration function matlab
上传时间： 2016-08-31
上传用户：米卡
Matrix iteration Methods. Matlab Implementation.
标签： Implementation iteration Methods Matrix
上传时间： 2013-12-18
上传用户：anng
Fixed-Point iteration
标签： Fixed-Point iteration
上传时间： 2017-08-21
上传用户：jennyzai
Solving linear equations using iteration. Seidels and Biggest incline methods
标签： equations iteration Solving Seidels
上传时间： 2013-12-25
上传用户：playboys0
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
标签： instantaneous algorithm Bayesian Gaussian
上传时间： 2013-12-19
上传用户：jjj0202
calculatePXTheta---Calculate the probability of each pixel being its color conditioned on all of the clusters that were found at the previous (coarser) iteration.
标签： calculatePXTheta probability conditioned Calculate
上传时间： 2013-12-24
上传用户：lyy1234
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit conditional dependencies between parts of the state to estimate. By doing so, RBPFs can improve the estimation quality while also reducing the overall computational load in comparison to original PFs. However, the computational complexity is still too high for many real-time applications. In this paper, we propose a modified RBPF that requires a single Kalman Filter (KF) iteration per input sample. Comparative experiments show that while good convergence can still be obtained, computational efficiency is always drastically increased, making this algorithm an option to consider for real-time implementations.
标签： Particle Filters Rao-Blackwellised exploit
上传时间： 2016-01-02
上传用户：refent
