This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". 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： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
標簽： algorithms problems Several trivial
上傳時間： 2014-01-20
上傳用戶：royzhangsz
This folder has some scritps that you may find usefull. All of it comes from questions that I ve received in my email. If you have a new request/question, feel free to send it to marceloperlin@gmail.com. But please, don t ask me to do your homework. Passing_your_param0 This folder contains instructions (and m files) for passing you own initial parameters to the fitting function. I also included a simple simulation script that will create random initial coefficients (under the proper bounds) and fit the model to the data.
標簽： that questions scritps usefull
上傳時間： 2013-12-28
上傳用戶：netwolf
JLAB is a set of Matlab functions I have written or co-written over the past fifteen years for the purpose of analyzing data. It consists of four hundred M-Files spanning thirty thousand lines of code. JLAB includes functions ranging in complexity from one-line aliases to high-level algorithms for certain specialized tasks. These have been collected together and made publicly available for you to use, modify, and --- subject to certain very reasonable constraints --- to redistribute. Some of the highlights are: a suite of functions for the rapid manipulation of multi-component, potentially multi-dimensional datasets a systematic way of dealing with datasets having components of non-uniform length tools for fine-tuning figures using compact, straightforward statements and specialized functions for spectral and time / frequency analysis, including advanced wavelet algorithms developed by myself and collaborators.
標簽： co-written functions the fifteen
上傳時間： 2014-01-26
上傳用戶：hjshhyy
超寬帶UWB,包括:uwb.mdl: UWB model - open this to begin. uwb_lib.mdl: Library blocks for UWB model. uwb_init.m: Initialization called before model is loaded. uwb_settings: Sets up structure containing system parameters ( uwb in workspace). uwb_imr.m: Initializes UWB channel impulse response. uwb_sv_*.m: Four M-Files used to generate channel impulse responses (MAT files).
上傳時間： 2016-10-12
上傳用戶：gaome
Written for engineering and computer science students and practicing engineers, this book provides the fundamental applications and mathematical techniques of signal processing. Topics covered include programming in MATLAB, filters, networking, and parallel processing. MATLAB is introduced and used to solve numerous examples in the book. Companion software available In addition, a set of MATLAB M-Files is available on a CD bound in the book.
標簽： engineering practicing and engineers
上傳時間： 2017-07-28
上傳用戶：fandeshun
Included are the files wav1.m, wav2.m, wavecoef.mat and readme. wav2 function implements the tree structured wavelet transform of the input matrix, up to the given level of decomposition. Wav2 uses another function called wav1, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coefficients are used for wavelet transform operation wahich is saved in wavcoeff.mat.
標簽： implements the wav Included
上傳時間： 2015-06-23
上傳用戶：愛死愛死
There are two files in the zip folder. bpsk_spread.m and jakesmodel.m Steps for simulation: 1] Run jakesmodel.m first 2] Then run bpsk_spread.m . 3] Note that during the first run bpsk_spread.m has no rayleigh fading.This is because the corresponding code has been commented 4] The resulting performance is stored in BER_awgn. 5] Now uncomment the Rayleigh Fading code in bpsk_spread.m file. 6] Same time comment BER_awgn (line 112) and uncomment BER_ray variable. 7] Run the simulation. To compare the perfromances of the receiver using DSSS plot the BER_awgn and BER_ray
標簽： bpsk_spread jakesmodel simulation folder
上傳時間： 2016-05-19
上傳用戶：ynsnjs
* acousticfeatures.m: Matlab script to generate training and testing files from event timeseries. * afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries. * extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information. * extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes. * sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries. * ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module. ?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module. ?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module.
標簽： acousticfeatures timeseries generate training
上傳時間： 2013-12-26
上傳用戶：牛布牛
多目標遺傳算法程序 to run Demo files, is to run SGALAB_demo_*.m what s new: 1) Multiple-Objective GAs VEGA NSGA NPGA MOGA 2) More TSP mutation and Crossover methods PMX OX CX EAX Boolmatrix 3) More selection methods Truncation tornament stochastic 4) mutation methods binary single point int/real single point 5) encoding/decoding methods binary integer/real messy gray DNA permuation to fix the plot bugs for 4001 , download this file and replace old files.
標簽： Multiple-Objective SGALAB_demo run files
上傳時間： 2013-12-21
上傳用戶：mhp0114
