On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
本文簡要介紹一種基于Monte Carlo模型和時隙驅動相結合的WCDMA R99 HSDPA網絡規劃仿真模型，并通過該模型對一則案例在動態功率分配和靜態功率分配情況下分別進行仿真。在最后部分，文章給出仿真結果，討論聯合載頻和獨立載頻吞吐量差別，并分析功率分配方式對下行吞吐量和資源利用率的影響。
上傳時間： 2013-11-08
上傳用戶：ljmwh2000
超寬帶系統鏈路matlab仿真程序 超寬帶系統簡單仿真平臺，有簡單界面.包括可替換的脈沖成型（半余弦脈沖）、IEEE802.15.3a的修正SV信道、最大似然信道估計、Rake接收機等模塊，可以實現monte carlo仿真求誤碼率。可添加多址接入、編碼等功能（維特比編解碼、幀同步的程序由本人同學編寫）。入口主程序uwbsim.m 編解碼程序：bin2deci.m；bini2deci.m；deci2bin.m VITRBI.m 信道及信道估計：ch_est.m；channel.m；channelgenerator.m；conv_m. m；sigfold.m UWB_SV_channel.m uwb_sv_cnvrt_ct.m uwb_sv_eval_ct.m uwb_sv_model_ct.m uwb_sv_params.m Rake接收機：findpeak.m；MRC_combine.m；MRC_Rake.m；n_upsample.m；selectpath.m 其他：cnv_encd.m；dssignal.m；Eb_halfcos.m；waveshape.m；halfcos_generator.m；metric.m；nxt_stat.m；sim_main.m；spreadgren.m；test_code.m；training_ds.m；uwbsim.m；vit_test.m
上傳時間： 2013-12-30
上傳用戶：duoshen1989
Electromagnetic scattering from the trees above a tilted rough ground plane generated by the stochastic Lidenmayer system is studied by Monte Carlo simulations in this paper.The scattering coefficients are calculated in three methods:coherent addition approximation,tree-independent scattering,and independent scattering.
標簽： Electromagnetic scattering generated the
上傳時間： 2013-12-06
上傳用戶：xieguodong1234
Klaas Gadeyne, a Ph.D. student in the Mechanical Engineering Robotics Research Group at K.U.Leuven, has developed a C++ Bayesian Filtering Library that includes software for Sequential Monte Carlo methods, Kalman filters, particle filters, etc.
標簽： Engineering Mechanical Robotics Research
上傳時間： 2015-09-07
上傳用戶：Altman
自己編寫的超寬帶系統簡單仿真平臺，有簡單界面.包括可替換的脈沖成型（半余弦脈沖）、IEEE802.15.3a的修正SV信道、最大似然信道估計、Rake接收機等模塊，可以實現monte carlo仿真求誤碼率?？商砑佣嘀方尤搿⒕幋a等功能（維特比編解碼、幀同步的程序由本人同學編寫）。入口主程序uwbsim.m 編解碼程序：bin2deci.m；bini2deci.m；deci2bin.m VITRBI.m 信道及信道估計：ch_est.m；channel.m；channelgenerator.m；conv_m. m；sigfold.m UWB_SV_channel.m uwb_sv_cnvrt_ct.m uwb_sv_eval_ct.m uwb_sv_model_ct.m uwb_sv_params.m Rake接收機：findpeak.m；MRC_combine.m；MRC_Rake.m；n_upsample.m；selectpath.m 其他：cnv_encd.m；dssignal.m；Eb_halfcos.m；waveshape.m；halfcos_generator.m；metric.m；nxt_stat.m；sim_main.m；spreadgren.m；test_code.m；training_ds.m；uwbsim.m；vit_test.m
上傳時間： 2013-12-15
上傳用戶：redmoons
Uniform random number generators by Agner Fog, 2001 - 2007 randomc.zip contains a C++ class library of uniform random number generators of good quality. The random number generators found in standard libraries are often of a poor quality, insufficient for large Monte Carlo calculations. This C++ implementation provides random number generators of a much better quality: Better randomness, higher resolution, and longer cycle lengths. The same random number generators are available as libraries coded in assembly language for higher speed. These libraries can be linked into projects coded in other programming languages under Windows, Linux, BSD, etc. The library files are available in the archive asmlib.zip. Non-uniform random number generators are provided in stocc.zip.
標簽： generators contains Uniform randomc
上傳時間： 2014-12-01
上傳用戶：royzhangsz
Computes BER v EbNo curve for convolutional encoding / soft decision Viterbi decoding scheme assuming BPSK. Brute force Monte Carlo approach is unsatisfactory (takes too long) to find the BER curve. The computation uses a quasi-analytic (QA) technique that relies on the estimation (approximate one) of the information-bits Weight Enumerating Function (WEF) using A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
標簽： convolutional Computes encoding decision
上傳時間： 2013-12-24
上傳用戶：咔樂塢
pMatlab is a toolsbox from MIT for running matlab in parallel style on a multi-core PC or a cluster environment. These two documents summary the usage of pMatlab and running time measurements on three simple Monte Carlo simulation codes.
標簽： multi-core toolsbox parallel pMatlab
上傳時間： 2014-12-05
上傳用戶：zhliu007
The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field approximation, and 4 MONTE-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
標簽： Matlab-interfaces inference interface the
上傳時間： 2016-08-27
上傳用戶：gxrui1991
