gibbs，beyesian network，intelligent inference, Markov, BeliefPropagation. It is a very good surce code for intelligent reasoning research
標簽： gibbs
上傳時間： 2014-01-15
上傳用戶：372825274
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
利用二元域的高斯消元法得到輸入矩陣H對應的生成矩陣G，同時返回與G滿足mod(G*P ,2)=0的矩陣P，其中P 表示P的轉置 使用方法：[P，G]=Gaussian(H,x)，x=1 or 2,1表示G的左邊為單位陣
上傳時間： 2014-11-27
上傳用戶：semi1981
這是一個非常簡單的遺傳算法源代碼，代碼保證盡可能少，實際上也不必查錯。對一特定的應用修正此代碼，用戶只需改變常數的定義并且定義“評價函數”即可。注意代碼 的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區別。該系統使用比率選擇、精華模型、單點雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應該命名為‘gadata.txt’；系統產生的輸出文件為‘galog.txt’。輸入的 文件由幾行組成：數目對應于變量數。且每一行提供次序——對應于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，等等。
上傳時間： 2015-10-16
上傳用戶：曹云鵬
基于libsvm，開發的支持向量機圖形界面(初級水平)應用程序，并提供了關于C和sigma的新的參數選擇方法，使得SVM的使用更加簡單直觀.參考文章 Fast and Efficient Strategies for Model Selection of Gaussian Support Vector Machine 可google之。
標簽： libsvm
上傳時間： 2015-10-16
上傳用戶：cuibaigao
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
標簽： sequential simulation posterior overview
上傳時間： 2015-12-31
上傳用戶：225588
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標簽： monitoring sequential industria accurate
上傳時間： 2013-12-17
上傳用戶：familiarsmile
用于產生gamma分布的噪聲序列，以及分析gaussian噪聲的各參數。
上傳時間： 2016-01-08
上傳用戶：xfbs821
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
