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电子书籍  ASP开发指南,里面的西很不错的哟.This book is for anyone who wants to learn about using .NET for web interface desi

ASP开发指南,里面的西很不错的哟.This book is for anyone who wants to learn about using .NET for web interface design. Beginner or hobbyist .NET developers can certainly get a good foundation of .NET web interface design by going through this book from cover to cover. However, more seasoned .NET profe ...

https://www.eeworm.com/dl/cadence/ebook/260283.html

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其他  两种解决方案 Richard just finished building his new house. Now the only thing the house misses is a cute l

两种解决方案 Richard just finished building his new house. Now the only thing the house misses is a cute little wooden fence. He had no idea how to make a wooden fence, so he decided to order one. Somehow he got his hands on the ACME Fence Catalogue 2002, the ultimate resource on cute little wooden ...

https://www.eeworm.com/dl/534/262857.html

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单片机开发  upsd_flash.c These functions are provided to help you develop your initial code. They are optim

upsd_flash.c These functions are provided to help you develop your initial code. They are optimized for speed rather that size. As a result, you will see very few nested function calls. If speed is not critical, you can use function calls for common tasks (like dat polling after writing a byte ...

https://www.eeworm.com/dl/648/276882.html

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人工智能/神经网络  n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional inde

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 ...

https://www.eeworm.com/dl/650/280629.html

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人工智能/神经网络  On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carl

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 deta ...

https://www.eeworm.com/dl/650/280633.html

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数学计算  The software implements particle filtering and Rao Blackwellised particle filtering for conditionall

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 generi ...

https://www.eeworm.com/dl/641/284170.html

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matlab例程  In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional ind

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 th ...

https://www.eeworm.com/dl/665/284182.html

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matlab例程  In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve r

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: ...

https://www.eeworm.com/dl/665/284186.html

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数学计算  This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps t

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, N ...

https://www.eeworm.com/dl/641/284866.html

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数学计算  This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hier

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 n ...

https://www.eeworm.com/dl/641/284868.html

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