Description: HMM(Hidden Markov Model)，狀態數目N=3，觀察符號數目M=2，時間長度T=3。
(a) Probability Evaluation: 給定狀態轉換機率A、狀態符號觀察機率B、和起始機率 ，求觀察序列 出現的機率。
(b) Optimal State Sequence: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率 、和觀察序列 ，求一個狀態序列 使得O出現的機率最大。
(c) Parameter Estimation: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率 、和觀察序列 ，求新的A、B、 ，使得O出現的機率最大。
-HMM (Hidden Markov Model), state the number of N = 3, Observation number of symbols M = 2, T = length of three. (A) Probability Evaluation : given state transition probability A, Observer status symbol probability of B, and initial probability for observation sequence in the octave. (B) Optimal State Sequence : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, for a state sequence of O makes the greatest risk. (C) Parameter Estimation : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, the way the A, B, and O makes the greatest risk. Platform: |
Size: 146567 |
Author:章勝鈞 |
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Description: HMM(Hidden Markov Model)，狀態數目N=3，觀察符號數目M=2，時間長度T=3。
(a) Probability Evaluation: 給定狀態轉換機率A、狀態符號觀察機率B、和起始機率 ，求觀察序列 出現的機率。
(b) Optimal State Sequence: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率 、和觀察序列 ，求一個狀態序列 使得O出現的機率最大。
(c) Parameter Estimation: 給定狀態轉換機率A、狀態符號觀察機率B、起始機率 、和觀察序列 ，求新的A、B、 ，使得O出現的機率最大。
-HMM (Hidden Markov Model), state the number of N = 3, Observation number of symbols M = 2, T = length of three. (A) Probability Evaluation : given state transition probability A, Observer status symbol probability of B, and initial probability for observation sequence in the octave. (B) Optimal State Sequence : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, for a state sequence of O makes the greatest risk. (C) Parameter Estimation : given state transition probability A, Observer status symbol probability of B, the initial probability, and observation sequence, the way the A, B, and O makes the greatest risk. Platform: |
Size: 146432 |
Author:章勝鈞 |
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Description: 当年为了学隐马尔可夫模型，所以边看边翻译的，如果那儿没翻译对，希望能给指出来，有利后来的人理解！
参考文献我没有删，就留下给看看吧！因为不能是中文标题，我改为英文的吧．-Year in order to learn hidden Markov models, so while watching the side of the translation, if there did not translate right, hope that they will be pointed out to be beneficial to understanding later! I did not delete references on the left to see it! Because should not the Chinese title, I read it in English. Platform: |
Size: 204800 |
Author:daki |
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Description: Markov参数是系统能观测性规范型实现时的重要参数，他给出了系统能观测性规范型的一种简单的实现方法。给出了Markov参数的定义、算法流程及运行算例，最后给出源程序！-Markov parameter is the system to standardize the type of observation when the realization of the important parameters, he gives the observation system can standardize the type of a simple method. Given the definition of Markov parameters, the algorithm processes and run an example, given the source code! Platform: |
Size: 23552 |
Author:yzy |
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Description: A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces Platform: |
Size: 8192 |
Author:曹大群 |
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Description: 提出了一种基于函数联接的感知器神经网络的纹理分类方法.它采用高斯2马尔柯夫随机场模型(GM RF)对纹理进行描述,模型参数即为纹理特征,参数估计采用最小平方误差方法获得.将估计参数作为表达纹理的特征向量,用感知器网络对特征进行分类,并且采用函数联接的方式解决线性不可分问题.对纹理图象进行的实验表明,采用这种方法能够提高学习速度,简化计算过程,并取得较好的纹理分类效果.
-Based on the function connected perceptron neural network texture classification method. It uses 2 Gaussian Markov Random Field Model (GM RF) to describe the texture, the model parameters is the texture feature, parameter estimation using least squares error obtained. the estimated parameters as the expression of texture feature vector, using the characteristics of sensor networks for classification, and the use of function to resolve connection problems can not be separated from linear. of texture images of the experiments show that this approach can enhance the learning speed, to simplify the calculation process and obtain a better effect of texture classification. Platform: |
Size: 285696 |
Author:singro jiang |
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Description: EM算法（英文）A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models-A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models Platform: |
Size: 99328 |
Author:雷雷 |
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Description: EM算法简明教程 用于高斯分布隐马尔可夫模型的参数估计-Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models Platform: |
Size: 99328 |
Author:hou |
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Description: HTS version 2.1 includes hidden semi-Markov model (HSMM) training/adaptation/synthesis, speech parameter generation algorithm considering global variance (GV), SMAPLR/CSMAPLR adaptation, and other minor new features. Platform: |
Size: 305152 |
Author:shouji |
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Description: 实现在参数先验是高斯分布情况下，利用马尔科夫链蒙特卡洛算法来对Logistic 回归模型参数的后验分布进行抽样
-It implements different Markov Chain Monte Carlo strategies for sampling from the posterior distribution over the parameter values for Logistic Regression models with a Gaussian prior on the parameter values. Platform: |
Size: 29696 |
Author:王而山 |
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Description: 用准蒙特卡洛方法，即Monte-Carlo-Markov-Chain计算似然函数，确定宇宙学参数。-To calculate the likelihood and find the best parameter set. Platform: |
Size: 14435328 |
Author:hao |
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Description: 这是张德丰《MATLAB概率与数理统计分析》随书源码(M文件)。代码包括的内容有：概率分布计算及统计特征、数字特征计算、统计图绘制(盒状图、散度图等)、点估计和区间估计、假设检验、方差分析、曲线拟合、回归分析、因素分析、聚类分析、正交实验设计分析、多元方差分析、判别分析、隐马尔可夫模型建模与参数估计和在语音识别中的应用。-This is Zhang Defeng " MATLAB Analysis of probability and mathematical statistics," with the book source (M file). The contents of the code include: calculation of the probability distribution and statistical characteristics, digital feature calculation, statistical mapping (box-shaped graph, scatter graph, etc.), point and interval estimation, hypothesis testing, analysis of variance, curve fitting, regression analysis, factor analysis, cluster analysis, orthogonal experimental design, multivariate analysis of variance, discriminant analysis, hidden Markov modeling and parameter estimation and application in speech recognition. Platform: |
Size: 32768 |
Author:吴 |
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Description: Image segmentation based on fusion of edge information and region growing with the use of Markov random fields Platform: |
Size: 4096 |
Author:Anton |
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Description: Alchemy: a tool in c++ for markov logic network inference, parameter learning and structure learning
Platform: |
Size: 12003328 |
Author:xyz1712 |
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Description: A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models Platform: |
Size: 88064 |
Author:asif |
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Description: 基于隐马尔可夫模型的语音单字识别研究：本文针对线性模型在语音识别中的不足, 进行了隐马尔可夫模型(HMM)在
语音单字识别中的研究,主要对观察输出概率求解、 最佳状态序列寻找、 参数估计和
模型参数的选择进行了探讨.-Based on hidden Markov model speech word recognition: the lack of the linear model in speech recognition, hidden Markov model (HMM) speech word recognition, mainly on the observation of the output probability for solving the most best state sequence search, the choice of the parameter estimation and model parameters are discussed. Platform: |
Size: 198656 |
Author:郭粉玉 |
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Description: 马尔科夫链的matlab仿真,马尔可夫模型（Markov Model，MM）是统计模型，它用来描述一个含有隐含未知参数的马尔可夫过程。-Matlab simulation of Markov chains, Markov models (Markov Model, MM) is a statistical model, which is used to describe an unknown parameter containing hidden Markov process. Platform: |
Size: 169984 |
Author:李想 |
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Search - Markov parameter