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n 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：
2013-12-17
上傳用戶：zhaiyanzhong
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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
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The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downLoading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, Load matlab and run the demo.
標簽：
filtering
particle
Blackwellised
conditionall
上傳時間：
2014-12-05
上傳用戶：410805624
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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
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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
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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
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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
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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
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This guide describes Freescale’s BeeKit Wireless Connectivity Toolkit configuration tool used for ZigBee, 802.15.4, or SMAC application development. The guide describes system requirements, installation, and how to launch BeeKit. The book then guides users through a simple step-by-step process of how to open and Load a demonstration project through the CodeWarrior IDE without further modification. This allows for a quick evaluation of Freescale’s ZigBee technology.
標簽：
configuration
Connectivity
Freescale
describes
上傳時間：
2013-12-24
上傳用戶：偷心的海盜
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UART發送TX控制電路設計，以波特率產生器的EnableTX將數據DATAO以Load信號將其送入發送緩沖器Tbuff，并令寄存器內容已載有數據而非空出的標志tmpTBufE=0。當同步波特率信號來臨時監視是否處于tmpTBufE=0（內有數據）以及tmpTRegE=1（沒有數據）。即處于尚未啟動發送態則將Tbuff緩沖寄存器 送入傳輸寄存器Treg內并令tmpTRegE=0（內又送入數據），但因Tbuff已轉送入緩沖寄存器TregE內，為空故令tmpTBufE=1，此tmpTBufE代表緩沖寄存器Tbuff是否為空可再予以送入新的要發送的數據。假如tmpTRegE=0（內有數據）則便要開始進行數據串行傳輸，傳出數據為8位，連同啟動信號“0”共需9位的發送計數，以BitCnt作計數。當BitCnt=0計數器便開始遞加計數字節，同時令起始信號為0，送入TxD輸出端輸出。而計數器為1-8時都將TReg的最低位Treg(0)輸出到TxD端，并令Treg[]作算術右移運算，依次將Treg[]的D7-D0通過D0移到TxD端輸出，直到第9位時停止移位，并將停止位TxD=0發送而結束一個8位數據的發送。
標簽：
UART
發送
控制
電路設計
上傳時間：
2016-06-23
上傳用戶：kristycreasy
