The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the Likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
標簽： algorithm Expectation-Maximization iterative optimi
上傳時間： 2015-06-17
上傳用戶：獨孤求源
A one-dimensional calibration object consists of three or more collinear points with known relative positions. It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general rigid motions synchronously, the camera set can be linearly calibrated. A linear algorithm for the camera set calibration is proposed，and then the linear estimation is further re¯ ned using the maximum Likelihood criteria. The simulated and real image experiments show that the proposed algorithm is valid and robust.
標簽： one-dimensional calibration collinear consists
上傳時間： 2014-01-12
上傳用戶：璇珠官人
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the Likelihood in dynamic models.
標簽： sequential simulation posterior overview
上傳時間： 2015-12-31
上傳用戶：225588
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum Likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
標簽： performance equalizers Adaptive several
上傳時間： 2016-02-16
上傳用戶：yan2267246
自己編的matlab程序。用于模式識別中特征的提取。是特征提取中的Sequential Forward Selection方法，簡稱sfs.它可以結合Maximum-Likelihood-Classifier分類器進行使用。
標簽： Sequential Selection Forward matlab
上傳時間： 2016-04-02
上傳用戶：ma1301115706
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
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-Likelihood decoder).
標簽： LDPC introduction simulation software
上傳時間： 2014-01-14
上傳用戶：大融融rr
Sequential Monte Carlo without Likelihoods 粒子濾波不用似然函數的情況下 本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable Likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.
標簽： Likelihoods Sequential Bayesian without
上傳時間： 2016-05-26
上傳用戶：離殤
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log Likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log Likelihood of estimates ％
標簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-Likelihood decoder).
標簽： LDPC introduction simulation software
上傳時間： 2014-12-05
上傳用戶：change0329
