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
移動ip書籍，很有用。 Third Generation (3G) mobile offers access to broadband multimedia services - and in the future most of these, even voice and video, will be IP-based. However 3G NETWORKS are not based on IP technologies, rather they are an evolution from existing 2G NETWORKS. Much work needs to be done to IP QoS and mobility protocols and architectures for them to be able to provide the functionality 3G requires.
上傳時間： 2013-12-16
上傳用戶：moerwang
aiNet application is a very powerful and a very simple tool for solving the problems which are usually solved with artificial neural NETWORKS (ANN). All possible tests we had run proved that the results obtained with aiNet are at least as good as the results obtained with some other ANNs. Let us state some of aiNet抯 features. (c) aiNet 1995-1997
標簽： very application powerful problems
上傳時間： 2014-01-16
上傳用戶：wang5829
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 paper presents the results of the Finnish national "Technology Vision of the Future Distribution Network" project. The aim of the project was to create a technology vision of future distribution NETWORKS. Because the life span of NETWORKS is very long, a long term vision is very important for guiding network investments and technology development.
標簽： the Distributio Technology national
上傳時間： 2013-12-18
上傳用戶：李彥東
After an introduction to the history of mobile communications, this guide considers the basics of mobile radio, the design of cellular and private radio systems, and issues of interworking with the fixed network. It then looks at the role of the mobile radio operator, the design and operation of mobile radio NETWORKS, the needs of large user groups, and relevant regulatory and government decisions. A final section focuses on career development through resolution of conflicts, understanding managerial issues, and professional vehicles.
標簽： communications introduction considers the
上傳時間： 2016-06-24
上傳用戶：nanshan
[wireless—Mobile Network] l MobiWan is a Mobile IPv6 extension for the NS simulator l MobiWan: NS-2 extensions to study mobility in Wide-Area IPv6 NETWORKS l NS2 + MobiWan2的安裝 l MobiWan的安裝 (From Wireless Netorwork Lab at Beijing University of Posts and Telecommunications) l Ant-like Mobile Agents - NS2 Patch l SUMO – Simulation of UrBan Mobility ( AN open source traffic simulation package)
標簽： MobiWan Mobile extension simulator
上傳時間： 2016-06-29
上傳用戶：縹緲
