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CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori estimation of two chain Coupled Hidden markov Models.
標簽： aposteriori University CHMMBOX version
上傳時間： 2014-01-23
上傳用戶：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.
標簽： conversation conversing simulators language
上傳時間： 2015-10-09
上傳用戶：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.
標簽： Hidden_markov_model_for_automatic speech_recognition implements left-right
上傳時間： 2016-01-23
上傳用戶：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.
標簽： Carlo Monte algorithm sampling
上傳時間： 2013-12-02
上傳用戶：jkhjkh1982
Sequential Monte Carlo without Likelihoods 粒子濾波不用似然函數的情況下 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.
標簽： Likelihoods Sequential Bayesian without
上傳時間： 2016-05-26
上傳用戶：離殤
無線通信的各種運動模型。適用于移動通信、無線傳感器網絡等領域。 包括：Random walk、random waypoint、random direction、boundless simulation area、 gauss-markov等運動模型 - probabilistic random walk
標簽： random simulation direction boundless
上傳時間： 2014-11-12
上傳用戶：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
標簽： Application Estimation Algorithm Parameter
上傳時間： 2017-09-27
上傳用戶：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.
標簽： Telecommunications Processing Signal for
上傳時間： 2020-06-01
上傳用戶：shancjb
本書全面而系統地介紹了 MATLAB 算法和案例應用，涉及面廣，從基本操作到高級算法應用，幾乎 涵蓋 MATLAB 算法的所有重要知識。本書結合算法理論和流程，通過大量案例，詳解算法代碼，解決具 體的工程案例，讓讀者更加深入地學習和掌握各種算法在不同案例中的應用。 本書共 32 章。涵蓋的內容有 MATLAB 基礎知識、GUI 應用及數值分析、MATALB 工程應用實例、 GM 應用分析、PLS 應用分析、ES 應用分析、markov 應用分析、AHP 應用分析、DWRR 應用分析、 模糊逼近算法、模糊 RBF 網絡、基于 FCEM 的 TRIZ 評價、基于 PSO 的尋優計算、基于 PSO 的機構優 化、基本 PSO 的改進策略、基于 GA 的尋優計算、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 的尋優計算、基于 Bayes 的數據預測、基于 SOA 的 PID 參數整定、基于 BP 的人臉方向預測、基于 Hopfield 的數字識別、基于 DEA 的投入產出分析、基于 BP 的數據分類、基于 SOM 的數據分類、基于人工免疫 PSO 的聚類算法、 模糊聚類分析和基于 GA_BP 的抗糖化活性研究。 本書適合所有想全面學習 MATALB 優化算法的人員閱讀，也適合各種使用 MATALB 進行開發的工 程技術人員閱讀。對于相關高校的教學與研究，本書也是不可或缺的參考書。另外，對于 MATLAB 愛好 者，本書也對網絡上討論的大部分疑難問題給出了解答，值得一讀。
上傳時間： 2022-07-26
上傳用戶：
