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EMfor_neural_networks
- 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 Co
MCMC_Unscented_Particle_Filter_demo
- 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 Eri
bvp2
- 运用有限差分方法求解二阶边值问题，x" + a1*x’ + a0*x = u with x(t0) = x0, x(tf) = xf-solve BVP2 by the finite difference method
bvp2_shoot
- 运用打靶法求解二阶边值问题。BVP2: [x1,x2]’ = f(t,x1,x2) with x1(t0) = x0, x1(tf) = xf-solve BVP2 by the shooting method
upf_demos
- 无香粒子滤波的一个matlab例程，其中有ekf,ukf,pf,upf-In these demos, we demonstrate the use of the extended Kalman filter (EKF), unscented Kalman filter (UKF), standard particle filter (a.k.a. condensation, survival of the fittest, bootstrap filter, SIR, sequential M
EMdemo
- EM算法在神经网络中的应用，可以用来进行视频数据分类。-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 Wil
rjMCMCsa
- 可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络-Reversible Jump MCMC Bayesian Model Selection This demo demonstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats t