From helping to assess the value of new medical treatments to evaluating the factors that affect our opinions and behaviors, analysts today are finding myriad uses for categorical data methods. In this book we introduce these methods and the theory behind them. Statistical methods for categorical responses were late in gaining the level of sophistication achieved early in the twentieth century by methods for continuous responses. Despite influential work around 1900 by the British statistician Karl Pearson, relatively little development of models for categorical responses occurred until the 1960s. In this book we describe the early fundamental work that still has importance today but place primary emphasis on more recent modeling approaches. Before outlining
標(biāo)簽： evaluating treatments the helping
上傳時間： 2014-01-25
上傳用戶：jennyzai
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(biāo)簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
Fast settling-time added to the already conflicting requirements of narrow channel spacing and low phase noise lead to Fractional4 divider techniques for PLL synthesizers. we analyze discrete "beat-note spurious levels from arbitrary modulus divide sequences including those from classic accumulator methods.
標(biāo)簽： settling-time requirements conflicting already
上傳時間： 2016-04-14
上傳用戶：liansi
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(biāo)簽： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：康郎
北京大學(xué)ACM比賽題目 Consider an infinite full binary search tree (see the figure below), the numbers in the nodes are 1, 2, 3, .... In a subtree whose root node is X, we can get the minimum number in this subtree by repeating going down the left node until the last level, and we can also find the maximum number by going down the right node. Now you are given some queries as "What are the minimum and maximum numbers in the subtree whose root node is X?" Please try to find answers for there queries.
標(biāo)簽： the Consider infinite numbers
上傳時間： 2013-12-16
上傳用戶：日光微瀾
Obtain the CDF plots of PAPR from an OFDM BPSK transmission specified per IEEE 802.11a specification Per the IEEE 802.11a specifications, we 52 have used subcarriers. Given so, the theoretical maximum expected PAPR is 52 (around 17dB). However, thanks to the scrambler, all the subcarriers in an OFDM symbol being equally modulated is unlikely. Using a small script, the cumulative distribution of PAPR from each OFDM symbol, modulated by a random BPSK signal is obtained
標(biāo)簽： specification transmission specified Obtain
上傳時間： 2016-04-24
上傳用戶：haoxiyizhong
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(biāo)簽： very application powerful problems
上傳時間： 2014-01-16
上傳用戶：wang5829
matlab軟件的仿真實列。 If you have any bugs and questions in our simulation programs, please e-mail to harada@ieee.org. we try to do our best to answer your questions.
標(biāo)簽： simulation questions programs matlab
上傳時間： 2014-01-24
上傳用戶：cmc_68289287
The control uses javascript to handle number formatting as you keep typing and does the validation as well. For one of our requirement, we needed a javascript based control that did formatting as the users kept typing. Google search only yielded script that did formatting on focus change events only. So I went about creating a new control myself.
標(biāo)簽： javascript formatting validation control
上傳時間： 2016-05-15
上傳用戶：lingzhichao
s file contains the Joone Distributed training Environment (DTE). See http://www.jooneworld.com/docs/dte.html to learn more about it. To learn more about Joone - Java Object Oriented Neural Engine: http://www.joone.org Joone and the DTE are both released with the LGPL license @2004 Paolo Marrone and the Joone team - All rights reserved ==================================================================== Credits The Joone DTE uses the following external packages: - SUN Jini Network Technology http://wwws.sun.com/software/jini/index.html - Computefarm Framework http://computefarm.jini.org - Spring Framework http://www.springframework.org we want to thank all the authors and contributors of the above packages. Please read the respective licenses contained in this distribution.
標(biāo)簽： Distributed Environment jooneworld contains
上傳時間： 2013-12-25
上傳用戶：釣鰲牧馬
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