Search - aic mdl

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[Other resource] ReversibleJumpMCMCSimulatedAnneaing DL : 0: This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL
Update : 2008-10-13 Size : 935.87kb Publisher : 郭剑辉
[Other resource] least_square_estimation_model_select DL : 0: 生成一组带有高斯噪声的样本,分别用一阶,二阶,三阶的最小二乘估计方法进行拟合,然后分别用AIC,MDL,FPE,CAT四种评测模型对其性能进行比较,得到最优的拟合模型.
Update : 2008-10-13 Size : 984byte Publisher : 狐狸
[Mathimatics-Numerical algorithms] deternum1 DL : 0: AIC AND MDL 算法，输入为特征值
Update : 2008-10-13 Size : 1.35kb Publisher : 王
[Other] AIC DL : 0: AIC准则用于信源数估计 matlab源程序-criteria for the AIC source estimates Matlab source
Update : 2025-03-07 Size : 1kb Publisher : 可难
[AI-NN-PR] ReversibleJumpMCMCSimulatedAnneaing DL : 0: This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL
Update : 2025-03-07 Size : 936kb Publisher : 大辉
[Algorithm] least_square_estimation_model_select DL : 0: 生成一组带有高斯噪声的样本,分别用一阶,二阶,三阶的最小二乘估计方法进行拟合,然后分别用AIC,MDL,FPE,CAT四种评测模型对其性能进行比较,得到最优的拟合模型.
Update : 2025-03-07 Size : 1kb Publisher : 狐狸
[Mathimatics-Numerical algorithms] deternum1 DL : 0: AIC AND MDL 算法，输入为特征值-AIC AND MDL algorithm, input eigenvalue
Update : 2025-03-07 Size : 1kb Publisher : 王
[Other] mdl DL : 0: 读取MDL模型数据，很详细，可以简单修改就可以完成3D模型动画-MDL model data to read, very detailed and simple modifications can be completed on the 3D model animation
Update : 2025-03-07 Size : 254kb Publisher : xujianwei
[Windows Develop] AIC_MDL_or DL : 0: estimate the number of source using AIC and MDL criterion.
Update : 2025-03-07 Size : 1kb Publisher : jiang
[matlab] MDLAIC DL : 0: 在MATLAB环境下开发的基于AIC和MDL准则的远场入射新号的数目估计，估计准确度都很高，仿真效果理想-Developed under the MATLAB environment, based on AIC and MDL criteria for the far-field incident estimated number of new numbers, it is estimated have a high accuracy, simulation results are satisfactory
Update : 2025-03-07 Size : 1kb Publisher : 金江
[matlab] xinyuanshumu DL : 1: 阵列信号的信源数目估计——AIC，MDL，HQ，EDC法得比较-Array signal source number estimation- AIC, MDL, HQ, EDC method comparatively
Update : 2025-03-07 Size : 1kb Publisher : shenhanyou
[Software Engineering] signal DL : 0: signal processing codes very helpful to everone implements important algorithms such as music, beamforming, aic, mdl-signal processing codes very helpful to everone implements important algorithms such as music, beamforming, aic, mdl..
Update : 2025-03-07 Size : 7kb Publisher : ankit
[AI-NN-PR] Source_No_AIC_MDL_HQ_Prob_vs_SNR_ULA DL : 0: <空间谱估计理论与算法> 信号源个数估计 AIC MDL HQ 算法-spatial spectrum estimation source number estimation AIC MDL HQ
Update : 2025-03-07 Size : 1kb Publisher : 朱全江
[matlab] ddbmusic DL : 0: 不同信噪比下，利用MUSIC算法实现AIC,MDL,HQ,EDC-Different signal to noise ratio, the use of MUSIC algorithm AIC, MDL, HQ, EDC
Update : 2025-03-07 Size : 1kb Publisher : 34chendong
[matlab] DOA_zhunze DL : 0: DOA信号源估计，包含利用AIC,MDL,EDC,HQ四种准则，对于DOA估计的编程相当有用！malab程序验证-
Update : 2025-03-07 Size : 1kb Publisher :
[matlab] source-number-estimation DL : 0: 源信号数目估计MATLAB程序，包括IAIC,AIC,MDL,IMDL，MEVARC等5种算法，可得到5种算法在不同信噪比条件下的估计准确率性能曲线。-The number of source signals to estimate the MATLAB program, including five kinds of IAIC, AIC, MDL, IMDL, MEVARC algorithm available algorithms estimated accuracy in different SNR performance curve.
Update : 2025-03-07 Size : 5kb Publisher : 付卫红
[Other] AIC DL : 0: 1. 随机过程：，其中是均值为零、方差为1的白噪声，、是相互独立并在上服从均匀分布的随机相位。采用AIC和MDL准则估计信号源个数，并且画出相应的MUSIC频率估计谱线。要求：信号样本数为1000，估计的自相关矩阵为8阶。分析：利用AIC准则和MDL准则算出信号源个数，然后根据算出的信号源个数计算出MUSIC谱。 -AIC criteria and guidelines for the use of MDL calculates the number of signal sources, and then calculate the MUSIC spectrum calculated based on the number of signal sources.
Update : 2025-03-07 Size : 3kb Publisher : 蔡蓉
[Compress-Decompress algrithms] AIC-MDL-GDE DL : 1: 用AIC MDL GDE等方法估计源信号个数，并进行性等分析在白噪声和色噪声环境下-estimate the number of source signal using AIC MDL GDE
Update : 2025-03-07 Size : 3kb Publisher : 曾
[matlab] AIC_MDL DL : 0: AIC & MDL The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection. AIC is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. In doing so, it deals with the trade-off between the goodness of fit of the model and the complexity of the model. AIC does not provide a test of a model in the sense of testing a null hypothesis i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.-AIC & MDL The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection. AIC is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. In doing so, it deals with the trade-off between the goodness of fit of the model and the complexity of the model. AIC does not provide a test of a model in the sense of testing a null hypothesis i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.
Update : 2025-03-07 Size : 1kb Publisher : Said
[Algorithm] aic DL : 0: 基于信息论准则条件下的信源个数估计，AIC,MDL,HQ准则-this is used to estimate number of sources,AIC,MDL,HQ
Update : 2025-03-07 Size : 1kb Publisher : 123456

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