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Description: 最大似然（ML）准则和最大后验概率（MAP）准则Matlab仿真-Maximum Likelihood (ML) criteria and maximum a posteriori probability (MAP) criteria Matlab simulation
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Description: 卷积码的最大后验概率译码算法的一个验证matlab算法，必有注释。-Convolutional codes of maximum a posteriori probability decoding algorithm matlab an authentication algorithm, there is the Notes.
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Author: blackdeath |
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Description: 最优多用户检测，即最大后验概率的算法，主要是用于多用户检测中-Optimal multi-user detection, that is, maximum a posteriori probability algorithm, mainly used for multi-user detection
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Author: yuekeqiang |
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Description: 最大后验概率的vertbi算法的译码算法-Maximum a posteriori probability decoding algorithm vertbi algorithm
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Size: 4096 |
Author: yuekeqiang |
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Description: 这个Matlab程序实现了MAP算法-最大后验概率算法，同时包括对算法有：卷积编码、卷积解码，BPSK，AWGN。同时绘制它的误码率和SNR(信噪比)。-The Matlab program achieved a MAP algorithm- maximum a posteriori probability algorithm, at the same time including the algorithm are: convolutional coding, convolution decoding, BPSK, AWGN. At the same time rendering its error rate and SNR (signal to noise ratio).
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Author: 小单 |
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Description: 连续相位调制CPM的最大后验概率MAP解调-Continuous phase modulation CPM maximum a posteriori probability of the MAP demodulator
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Size: 2048 |
Author: 刘贤 |
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Description: 神经网络 高斯分布 最大后验估计 最大似然估计-Neural network Gaussian maximum a posteriori estimate maximum likelihood estimate
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Size: 1024 |
Author: 吴燕玲 |
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Description: 最大似然和最大后验概率算法描述，比较性能，仿真分析-Maximum likelihood and maximum a posteriori probability algorithm description, comparative performance, simulation analysis
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Author: 刘斐 |
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Description: MAPSAC指最大后验一致算法（Maximum a Posteriori sample consensus），是一种新的鲁棒性估计方法。由P.H.S Torr编写，供研究F阵鲁棒估计的人学习调用。-MAPSAC refers to maximum a posteriori agreement algorithm (Maximum a Posteriori sample consensus), is a new robust estimation method. By PHS Torr prepared for the study of robust estimation array F study call people.
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Author: Zhongren Wang |
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Description: 基于MAP的红外图像超分辨率技术研究的硕士论文，文中主要采用最大后验概率完成超分辨率算法的图像重建。-MAP-based infrared image super-resolution technology research master' s thesis, the main use of maximum a posteriori probability of the completion of super-resolution image reconstruction algorithm.
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Author: 路月 |
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Description: The paper is about bounds for LDPC and LDGM codes under MAP. A new method for analyzing low density parity check (LDPC) codes and low density generator
matrix (LDGM) codes under bit maximum a posteriori probability (MAP) decoding is
introduced. The method is based on a rigorous approach to spin glasses developed by Francesco
Guerra. It allows to construct lower bounds on the entropy of the transmitted message conditional
to the received one. Based on heuristic statistical mechanics calculations, we conjecture
such bounds to be tight. The result holds for standard irregular ensembles when used over
binary input output symmetric channels.
The method is first developed for Tanner graph ensembles with Poisson left degree distribution.
It is then generalized to ‘multi-Poisson’ graphs, and, by a completion procedure, to
arbitrary degree distribution.
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Author: mike zhou |
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Description: ：为了使应力变异在顽健语音识别系统中能够达到较好的识别效果，研究了基于隐马
尔可夫模型(HMM)的自适应技术，提出了将最大后验概率(MAP)和最大似然回归方法(MLLR)用
于应力变异语音的自适应中。实验结果表明，与基本系统相比，两种方法均有效地提高系统识别
率。以SD为初始模型的最大后验概率方法在150个训练样本时识别效果最好，可以达到90．4％ 。-: In order to stress variation in the robustness of speech recognition system can achieve better recognition results, based on Hidden Markov Model (HMM) of adaptive technology, put forward a maximum a posteriori probability (MAP) and Maximum Likelihood regression (MLLR) for the stress of the adaptive variation in voice. The experimental results show that compared with the basic system, both methods are effective to improve the system recognition rate. SD as the initial model to the maximum a posteriori probability method in 150 training samples to identify the best, can reach 90.4 .
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Author: 尹江波 |
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Description: 在统计学中，最大后验（英文为Maximum a posteriori，缩写为MAP）估计方法根据经验数据获得对难以观察的量的点估计。它与最大似然估计中的 Fisher 方法有密切关系，但是它使用了一个增大的优化目标，这种方法将被估计量的先验分布融合到其中。所以最大后验估计可以看作是规则化（regularization）的最大似然估计。
-In statistics, the maximum a posteriori (English as a Maximum a posteriori, abbreviated as MAP) estimation method according to empirical data is difficult to obtain right amount of observation point estimate. It is with the maximum likelihood estimation of the Fisher method is closely related to, but it uses a larger optimization goals, this approach would be the estimated amount of integration of prior distribution to it. Therefore, maximum a posteriori estimates can be regarded as regularization (regularization) of the maximum likelihood estimate.
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Author: youxia |
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Description: 这个Matlab程序实现了MAP算法-最大后验概率算法，同时包括对算法有：卷积编码、卷积解码，BPSK，AWGN。同时绘制它的误码率和SNR(信噪比)。
-The Matlab program achieved a MAP algorithm- maximum a posteriori probability algorithm, also include the algorithm are: convolutional coding, convolutional decoding, BPSK, AWGN. At the same time rendering its bit error rate and SNR (signal to noise ratio).
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Size: 1024 |
Author: 李娇 |
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Description: 通信仿真，二元位信号通过加性高斯白噪声的信道，经过匹配滤波，最大后验概率和最大似然概率检测比较-Communication simulation, binary bits signals through the additive gaussian white noise, through the channel is matched filtering, maximum a posteriori probability and the maximum likelihood detection probability
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Size: 2048 |
Author: 王峰 |
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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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Author: loossii |
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Description: 数字通信中的最大后验概率算法，课用于通信仿真。-Maximum Posteriori Probability
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Author: effieding |
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Description: 最大后验概率算法map的MATLAB程序-Algorithm for maximum a posteriori probability map of the MATLAB program
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Size: 1024 |
Author: 樊雯 |
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Description: 一种基于马尔科夫随机场的图像分割matlab源码，包含ICM迭代条件模式求解最大后验概率算法，已通过测试。-Markov random field based image segmentation matlab source code, including the ICM iteration conditions for solving the maximum a posteriori probability model algorithm has been tested.
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Size: 19456 |
Author: 包裹 |
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Description: 实现贝叶斯最大后验概率计算，适合原理的研究，简单易懂。-To achieve the maximum a posteriori Bayesian probability, the principle of appropriate and easily understood.
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Size: 13312 |
Author: louise |
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