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MCMC 马尔科夫链蒙特卡罗算法
- 这是关于MCM(马尔科夫链蒙特卡罗算法)方面的中文文章。-This is on the MCM (Markov chain Monte Carlo algorithm) aspects of Chinese articles
Anapplicationsystemofprobabilisticsoundsourcelocal
- 结合马尔可夫过程，提出一种概率论的声源定位算法，并给出了基于DSP的机器人实现。其中声源定位部分采用三个麦克风呈三角形放置，为减小由于噪声等引起的TDOA估计误差，采用马尔可夫过程计算时延，这样计算的时延会更可靠。该方案中的声源定位也属于一维定位，即只需知道声源的方向角-Combination of Markov process, a probability theory of sound source location algorithm, and give the robot based o
DTMC
- Foreign materials as well as Solving Discrete-Time Markov Chains,ece document.
rabiner
- 隐马尔科夫模型与语音识别的开山之作，google scholar中引用次数最多的论文-Hidden Markov model for speech recognition google scholar most cited papers
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- 这是一篇关于MCMC(马尔科夫链蒙特卡罗方法)应用于MIMO检测方面的文章（I-EEE）-This is an article on MCMC (Markov chain Monte Carlo method) is applied to MIMO detection for articles (I-EEE)
ieee_markov_ldpc
- A Partial Ordering of General Finite-State Markov Channels Under LDPC Decoding
QoS-of-markov-selection-strategy
- 面向QOS的马尔可夫选择决策算法，通过对算法模型合理化构建过程于异构环境特点的紧密结合，最大程度满足异构网络环境中用户QOS的长期效益-Facing the QOS markov selection decision algorithm, based on the rationalization process in constructing algorithm model heterogeneous environment characteristics of closely, satisfy
ch6
- 高斯马尔科夫过程相关文档。应用场景很多，如可用于对多径的建模。-the modelling using gauss-markov
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- 入了马尔科夫模型来计算词汇语义相似度， 实验证明，算法取得较理想的实验结果． 关-Into the Markov model to calculate the lexical semantic similarity Experiments show that the algorithm to obtain better experimental results. Close
GMM-GMR-v2.0
- a tutorial on hidden markov models and selected applications in speech recognition
empca.tar
- This tutorial gives a gentle introduction to Markov models and hiddenMarkov models (HMMs) and relates them to their use in automatic speech recognition.
mcmc
- 马尔可夫蒙特卡罗方法原理及MATLAB仿真入门级教程 适合新手-The Markov Chain Monte Carlo Simulations
Ahsanyour.glob.2009.pdf
- Three-Dimensional Markov Chain Model for Performance Analysis of the IEEE 802.11 Distributed Coordination Function
markov
- information theory markov chain
Gupta-and-Chen---2010---Theory
- This introduction to the expectation–maximization (EM) algorithm provides an intuitive and mathematically rigorous understanding of EM. Two of the most popular applications of EM are described in detail: estimating Gaussian mixture models (GMMs),
Markov-random-field
- 讲解马尔科夫随机场的基本理论，对随机场的性质进行详细介绍，完善理论 -Markov random field to explain the basic theory, along with details of the nature of the airport, perfect theory
HMRF-FCM
- 模式识别中的模糊聚类，采用隐马尔科夫链，考虑了先验概率，进行图像识别。-Pattern recognition, fuzzy clustering, using hidden Markov chain, taking into account a priori probability for image recognition.
Fixed-Point-Implementation
- Fixed-Point Implementation of Isolated Sub-Word Level Speech Recognition Using Hidden Markov Models
Hidden-Markov
- 关于隐马尔科夫模型方面的文献资料，介绍了HMM的原理，拓展。创新性将SVM和HMM结合应用-Literature on hidden Markov model aspects, introduced the principle of HMM expand. The innovative application of SVM and HMM combination
markov
- markov process Dunkan . K . Foly Definitions