這個(gè)代碼是policy Iteration算法關(guān)于強(qiáng)化學(xué)習(xí)的. 請(qǐng)您用winzip 解壓縮
標(biāo)簽： Iteration policy winzip 代碼
上傳時(shí)間： 2015-04-24
上傳用戶：lepoke
computing singular value of matrix by Iteration
標(biāo)簽： computing Iteration singular matrix
上傳時(shí)間： 2014-01-16
上傳用戶：lx9076
Based on Matlab,Gauss Iteration Method
標(biāo)簽： Iteration Matlab Method Based
上傳時(shí)間： 2016-02-03
上傳用戶：cjf0304
matlab function ---> find roots using fixed-point Iteration
標(biāo)簽： fixed-point Iteration function matlab
上傳時(shí)間： 2016-08-31
上傳用戶：米卡
Matrix Iteration Methods. Matlab Implementation.
標(biāo)簽： Implementation Iteration Methods Matrix
上傳時(shí)間： 2013-12-18
上傳用戶：anng
Fixed-Point Iteration
標(biāo)簽： Fixed-Point Iteration
上傳時(shí)間： 2017-08-21
上傳用戶：jennyzai
Solving linear equations using Iteration. Seidels and Biggest incline methods
標(biāo)簽： equations Iteration Solving Seidels
上傳時(shí)間： 2013-12-25
上傳用戶：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.
標(biāo)簽： instantaneous algorithm Bayesian Gaussian
上傳時(shí)間： 2013-12-19
上傳用戶：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.
標(biāo)簽： calculatePXTheta probability conditioned Calculate
上傳時(shí)間： 2013-12-24
上傳用戶：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.
標(biāo)簽： Particle Filters Rao-Blackwellised exploit
上傳時(shí)間： 2016-01-02
上傳用戶：refent
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