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Description: 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.
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Size: 198220 |
Author: 晨间 |
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Description: 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).
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Size: 7667 |
Author: 武博 |
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Description: ％ 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
％
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Size: 3416 |
Author: Shaoqing Yu |
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Description: 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).
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Size: 119205 |
Author: 张强 |
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Description: ldpc decode c程序
Iterative probabilistic decoding of linear block codes.Based on Pearl s Belief Propagation in Bayesian Networks.This version utilizes object to define graph nodes and LOG-LIKELIHOOD RATIOS. Much improved in terms of speed compared to log_pearl.c.
It can use a look-up table to avoid exp() computations
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Size: 4578 |
Author: sblkiaw |
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Description: 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.
-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.
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Size: 197632 |
Author: 晨间 |
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Description: 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). -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).
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Size: 7168 |
Author: 武博 |
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Description: ％ 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
％- 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
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Size: 3072 |
Author: Shaoqing Yu |
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Description: 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). -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).
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Size: 118784 |
Author: 张强 |
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Description: ldpc decode c程序
Iterative probabilistic decoding of linear block codes.Based on Pearl s Belief Propagation in Bayesian Networks.This version utilizes object to define graph nodes and LOG-LIKELIHOOD RATIOS. Much improved in terms of speed compared to log_pearl.c.
It can use a look-up table to avoid exp() computations-ldpc decode c procedure Iterative probabilistic decoding of linear block codes.Based on Pearl s Belief Propagation in Bayesian Networks.This version utilizes object to define graph nodes and LOG-LIKELIHOOD RATIOS. Much improved in terms of speed compared to log_pearl.c.It can use a look-up table to avoid exp () computations
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Size: 4096 |
Author: sblkiaw |
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Description: 由4个.m文件组成，用于16QAM和64QAM调制的软信息的提取-By 4. M files for 16QAM and 64QAM modulation of the soft information extraction
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Size: 2048 |
Author: wwyy |
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Description: 当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。- This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio
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Author: sun |
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Description: Log Likelihood Ratio (LLR) Objective Speech Quality Measure
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Size: 1024 |
Author: Mehrtash |
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Description: Several functions for evaluating the exact negative log-likelihood of ARMA models in O(n) time using the Kalman Filter
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Size: 4096 |
Author: Pippo |
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Description: 通过MATLAB软件；通过仿真得出性能曲线；从而比较bpsk的性能-The bit-error rate (BER) of binary phase-shift keying
in Rayleigh fading, using the Alamouti transmission scheme and
receiver selection diversity in the presence of channel-estimation
error, is studied. Closed-form expressions for the BER of log-likelihood
ratio selection, signal-to-noise ratio (SNR) selection, switchand-
stay combining selection, and maximum ratio combining are
derived in terms of the SNR and the cross-correlation coefficient
of the channel gain and its corrupted estimate. Two new selection
schemes, space–time sum-of-squares combining selection diversity
and space–time sum-of-magnitudes selection diversity, are
proposed and proven to provide almost the same performance as
SNR selection, but with much simpler implementations. The effects
of channel-
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Size: 340992 |
Author: 刘小洋 |
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Description: 对数似然比测度下低密度奇偶检验码的译码算法，有很大的价值啊 -Log-likelihood ratio measure under the low-density parity check codes decoding algorithm, there is great value to ah
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Size: 94208 |
Author: 张力 |
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Description: Algorithms for QAM Signal Classification Using Maximum Likelihood Approach Based on the Joint Probability Densities of Phases and amplitudes
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Size: 212992 |
Author: HASHEM |
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Description: illustrates the improvement in BER performance when using log-likelihood instead of hard decision demodulation in a convolutionally
coded communication link-illustrates the improvement in BER performance when using log-likelihood instead of hard decision demodulation in a convolutionally
coded communication link
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Size: 2048 |
Author: javad |
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Description: In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.-In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.
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Size: 2048 |
Author: loossii |
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Description: 用MATLAB编写代码,在经济和金融中法对连续时间过程实现了封闭的最大似然估计方法-The code, written in MATLAB, implements the closed-form maximum-likelihood estimation method for continuous-time processes in economics and finance. First, the code maximizes the log-likelihood function and displays the MLE estimates, the standard error estimates (constructed from the inverse of Hessian) and the misspecification-robust standard error (i.e., the sandwich estimate). Second, it reports whether the maximization procedure converges under the default tolerance. Finally, it plots the marginal log-likelihood for each parameter in a neighborhood of the estimates. The blue curve is the likelihood function, while each red star corresponds to the estimate for the corresponding parameter. For more information about the models, please read the user s guide (link "more").
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Author: 雪之恋 |
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