In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the Required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標簽： Rao-Blackwellised conditional filtering particle
上傳時間： 2013-12-14
上傳用戶：小儒尼尼奧
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the Required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
This demo nstrates 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： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：康郎
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
First of all we would like to thank God Almighty for giving us the strength and confidence in pursing the ambitions. We would like to thank our Examiner Professor Axel Jantsch for allowing us to do this under his guidance and encouragement. At the same time we would like to mention our sincere thanks to Mr. Said Zainali, Manager of FRAME ACCESS AB for giving all the Required equipment and the technical support which helped us to finish this thesis. We would like to mention our gratitude to our fellow VACS team members who helped us a lot during difficult times.
標簽： confidence Almighty strength giving
上傳時間： 2013-12-01
上傳用戶：小碼農lz
This is the library for all storage drivers. It simplifies writing a storage driver by implementing 90 percent of the code Required to support Plug and Play, Power Management, et cetera. This library is used by disk.sys, cdrom.sys and the tape class drivers.
標簽： storage implementing simplifies drivers
上傳時間： 2016-05-17
上傳用戶：我干你啊
SimpliciTI™ -1.0.3.exe for CC11xx and CC25xx SimpliciTI is a simple low-power RF network protocol aimed at small (<256) RF networks. Such networks typically contain battery operated devices which require long battery life, low data rate and low duty cycle and have a limited number of nodes talking directly to each other or through an access point or range extenders. Access point and range extenders are not Required but provide extra functionality such as store and forward messages. With SimpliciTI the MCU resource requirements are minimal which results in the low system cost.
標簽： SimpliciTI low-power network simple
上傳時間： 2014-11-05
上傳用戶：rishian
SimpliciTI™ -1.0.4.exe for CC2430 SimpliciTI is a simple low-power RF network protocol aimed at small (<256) RF networks. Such networks typically contain battery operated devices which require long battery life, low data rate and low duty cycle and have a limited number of nodes talking directly to each other or through an access point or range extenders. Access point and range extenders are not Required but provide extra functionality such as store and forward messages. With SimpliciTI the MCU resource requirements are minimal which results in the low system cost.
標簽： SimpliciTI low-power protocol network
上傳時間： 2016-05-21
上傳用戶：R50974
This discussion will attempt to outline some truths and common misconceptions about digital audio watermarking. It will survey the intrinsic obstacles that such technology is Required to overcome, shedding light on its performance criteria, compromises and limitations. While doing so, it will also survey a few common types of applications, hopefully leading to a clear understanding as to the appropriateness of such technology and its expertise within multimedia content protection.
標簽： misconceptions discussion attempt digital
上傳時間： 2016-07-18
上傳用戶：520
