CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori estimation of two chain Coupled Hidden markov Models.
标签： aposteriori University CHMMBOX version
上传时间： 2014-01-23
上传用户：rocwangdp
megahal is the conversation simulators conversing with a user in natural language. The program will exploit the fact that human beings tend to read much more meaning into what is said than is actually there MegaHAL differs from conversation simulators such as ELIZA in that it uses a markov Model to learn how to hold a conversation. It is possible to teach MegaHAL to talk about new topics, and in different languages.
标签： conversation conversing simulators language
上传时间： 2015-10-09
上传用户：lnnn30
Hidden_markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden markov models.
标签： Hidden_markov_model_for_automatic speech_recognition implements left-right
上传时间： 2016-01-23
上传用户：569342831
Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo algorithm to sample from the distribution P ~ EXP(-F), where F is the first argument to HMC. The markov chain starts at the point X, and the function GRADF is the gradient of the `energy function F.
标签： Carlo Monte algorithm sampling
上传时间： 2013-12-02
上传用户：jkhjkh1982
Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下 本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or markov chain Monte Carlo can be highly inefficient, and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.
标签： Likelihoods Sequential Bayesian without
上传时间： 2016-05-26
上传用户：离殇
无线通信的各种运动模型。适用于移动通信、无线传感器网络等领域。 包括：Random walk、random waypoint、random direction、boundless simulation area、 gauss-markov等运动模型 - probabilistic random walk
标签： random simulation direction boundless
上传时间： 2014-11-12
上传用户：libinxny
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
标签： Application Estimation Algorithm Parameter
上传时间： 2017-09-27
上传用户：dianxin61
This paper presents a Hidden markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.
标签： Telecommunications Processing Signal for
上传时间： 2020-06-01
上传用户：shancjb
本书全面而系统地介绍了 MATLAB 算法和案例应用，涉及面广，从基本操作到高级算法应用，几乎 涵盖 MATLAB 算法的所有重要知识。本书结合算法理论和流程，通过大量案例，详解算法代码，解决具 体的工程案例，让读者更加深入地学习和掌握各种算法在不同案例中的应用。 本书共 32 章。涵盖的内容有 MATLAB 基础知识、GUI 应用及数值分析、MATALB 工程应用实例、 GM 应用分析、PLS 应用分析、ES 应用分析、markov 应用分析、AHP 应用分析、DWRR 应用分析、 模糊逼近算法、模糊 RBF 网络、基于 FCEM 的 TRIZ 评价、基于 PSO 的寻优计算、基于 PSO 的机构优 化、基本 PSO 的改进策略、基于 GA 的寻优计算、基于 GA 的 TSP 求解、基于 Hopfield 的 TSP 求解、基 于 ACO 的 TSP 求解、基于 SA 的 PSO 算法、基于 kalman 的 PID 控制、基于 SOA 的寻优计算、基于 Bayes 的数据预测、基于 SOA 的 PID 参数整定、基于 BP 的人脸方向预测、基于 Hopfield 的数字识别、基于 DEA 的投入产出分析、基于 BP 的数据分类、基于 SOM 的数据分类、基于人工免疫 PSO 的聚类算法、 模糊聚类分析和基于 GA_BP 的抗糖化活性研究。 本书适合所有想全面学习 MATALB 优化算法的人员阅读，也适合各种使用 MATALB 进行开发的工 程技术人员阅读。对于相关高校的教学与研究，本书也是不可或缺的参考书。另外，对于 MATLAB 爱好 者，本书也对网络上讨论的大部分疑难问题给出了解答，值得一读。
上传时间： 2022-07-26
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