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This folder contains all the codes based on Matlab Language for the book <《Iterative Methods for Linear and Nonlinear Equations》, and there are totally 21 M files, which can solve most of linear and nonlinear equations problems.

it contains many classic Test Problems for Unconstrained Optimization such as camel6,treccani,goldstein,branin, shubert1,Ackley,dejong,dejong1,dejong2, dpower,rastrigin,Griewangk,Schwefel, rosenbrock2 and step. the package have the contour and mesh figures of these problem. it also give m files of t ...

％ Atomizer Main Directory, Version .802 里面信号含有分解去噪合成过程的代码 ％--------------------------------------- -------------------------------- ％ This is the main directory of the Atomizer package the full package ％ contains over 100 files, consisting of .m files, .mex files, and datasets. ...

CheckMate is a MATLAB-based tool for modeling, simulating and investigating properties of hybrid dynamic systems. Hybrid systems are modeled using the Simulink graphical user interface (GUI). Parameters and specifications are entered using both the Simulink GUI and user-defined m-files. CheckMate co ...

This file contains the material presented as the first Embedded MATLAB webinar on the MathWorks web site on September 13, 2007. It contains the PDF version of presentation slides and all necessary demonstration files (including MATLAB M-files and Simulink models).

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 ...

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 ...

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 ...

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 ...

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 ...