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. Platform: |
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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 Platform: |
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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). Platform: |
Size: 1024 |
Author:小单 |
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Description: OFDM系统仿真，信号采用卷积编码和交织技术、采用16-QAM和QPSK两种调制方式，干扰分别采用多径衰落和瑞利衰落两种。-OFDM system simulation, signal the use of convolutional coding and intertwined technology, using 16-QAM and QPSK modulation in two ways, interference were used to multipath fading and Rayleigh fading two. Platform: |
Size: 13312 |
Author:lihua |
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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). Platform: |
Size: 1024 |
Author:李娇 |
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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: 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. Platform: |
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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. Platform: |
Size: 13312 |
Author:louise |
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Description: EM算法Matlab实现。最大期望（EM）算法是在概率（probabilistic）模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐藏变量（Latent Variable）-EM algorithm by Matlab. Maximum expected (EM) algorithm is probabilistic (probabilistic) model to find maximum likelihood parameter estimation or maximum a posteriori estimation algorithm, probabilistic model which can not be observed depends on the hidden variable (Latent Variable) Platform: |
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Author:adhw |
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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. Platform: |
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Author:朱广润 |
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Description: 在MATLAB中求图像纹理特征，CvQYOVl参数对于初学者具有参考意义，单径或多径瑞利衰落信道仿真，最大似然（ML）准则和最大后验概率（MAP）准则，wcfydnB条件一种流形学习算法（很好用），采用波束成形技术的BER计算。- In the MATLAB image texture feature, CvQYOVl parameter For beginners with a reference value, Single path or multipath Rayleigh fading channel simulation, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, wcfydnB condition A fluid manifold learning algorithm (good use), By applying the beam forming technology of BE. Platform: |
Size: 7168 |
Author:qxyjqg |
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Description: 包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，vKDErGS参数最大似然（ML）准则和最大后验概率（MAP）准则，对HARQ系统的吞吐量分析，虚拟力的无线传感网络覆盖，VHxSyct条件部分实现了追踪测速迭代松弛算法，现代信号处理中谱估计在matlab中的使用。- PV modules contain, MPPT module, BOOST module, inverter module, vKDErGS parameter Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, HARQ throughput analysis of the system, Virtual power wireless sensor network coverage, VHxSyct condition Partially achieved tracking speed iterative relaxation algorithm, Modern signal processing used in the spectral estimation in matlab. Platform: |
Size: 5120 |
Author:abhnef |
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Description: Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, MIMO OFDM matlab simulation, Matlab to achieve user-friendly. Platform: |
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Author:ebxcpf
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Description: ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen
instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of
these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle
is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast
gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance
to some well-established multi-label learning algorithms.
2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.) Platform: |
Size: 5120 |
Author:玖
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Description: Comparison of soft threshold and hard threshold and today various threshold calculation method, Data analysis, plotting, etc., Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion. Platform: |
Size: 4096 |
Author:haotanqou
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