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Desktop2
- in includes backwar and baum-welch algorithms
hmm
- 此代码包含了HMM的backward算法，viterbi算法以及，前后向算法-This code includes backward,vitibi and Baum-Welch algorithm of HMM.
Hidden-Markov-algorithm
- 隐马尔可夫算法的实现，其中包括前向算法，后向算法，Viterbi算法，Baum-Welch算法-Hidden Markov algorithm, including backward algorithm, Viterbi algorithm, Baum-Welch algorithm to the algorithm,
BWDOHMM
- 標準Baum-Welch演算法之離散觀察HMM模型(基礎版本)-Implements the standard Baum-Welch (any-path) algorithm for discrete-observation HMMs.
matlab
- 了解隐马尔科夫模型HMM的概念、组成和需要解决的问题；通过matlab分析和三个基本算法分析读研和就业问题：forward算法、Viterbi算法和Baum-Welch算法-Understand the concept of Hidden Markov Model HMM, composition and problems to be solved matlab analysis by three basic algorithms:
matlab-experiment
- 了解隐马尔科夫模型HMM的概念、组成和需要解决的问题；掌握三个基本算法：forward算法、Viterbi算法和Baum-Welch算法，并利用matlab进行实验分析一道具体问题-Understand the concept of Hidden Markov Model HMM, composition and problems to be solved mastered three basic algorithms: forwar
Baum_Welch-algorithm
- 用Baum-Welch算法来迭代估计一个隐马尔科夫模型（HMM）的初始状态概率分布以及其状态转移概率矩阵。其中文件有mainfile_B_W.m为主函数，Baum_Welch.m为Baum-Welch算法迭代函数，Forward_variable.m与Backward_variable.m与Gamma_variable.m与Ksi_variable.m是需要计算的四种因子，B_pdf.m为混淆散射概率密度函数。-It s Baum-We
viterbiaEM
- 1.用隐马尔科夫模型（HMM）模拟肿瘤细胞整个染色体的拷贝数（CN）变异。并用viteri算法得到最可能的（CN）状态转移序列； 2.使用baum welch算法根据所给序列数据和初始状态转移矩阵，重新估算状态转移矩阵。-HMM, Hidden Markov, baulm welch, viterbi, SNP-array
HMM
- Hidden markov model with baum welch algo and vertibi algo
HMM
- 基于HMM模型的语音识别算法解析，里面含有汉语数码录音和其他各种训练算法，直接调用对比即可-HMM Viterbi Baum-Welch
HMM
- %函数名称：HMMTrain %参数：V-------训练观察序列（n X 1Cell矩阵），IPI,IA,IB-------模型参数初始值 %返回值：PI,A,B-------模型参数的学习结果 %函数功能：隐含马尔科夫模型的Baum-Welch学习算法(% function name: HMMTrain % parameters: V------- trains the observation sequence (n, X,
HMM
- 离散HMM的matlab程序，包含有前向—后向算法、 Baum-Welch算法以及Vertebi算法(The matlab program of discrete HMM, including forward-backward algorithm, Baum-Welch algorithm and Vertebi algorithm)
HMM-homework
- 隐马尔科夫实现，包含forward-hmm, Viterbi-hmm, Baum-Welch-hmm(Hidden Markov implementation, including forward-hmm, Viterbi-hmm, Baum-Welch-hmm)