gibbs，beyesian network，intelligent inference, Markov, BeliefPropagation. It is a very good surce code for intelligent reasoning research
标签： gibbs
上传时间： 2014-01-15
上传用户：372825274
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
利用二元域的高斯消元法得到输入矩阵H对应的生成矩阵G，同时返回与G满足mod(G*P ,2)=0的矩阵P，其中P 表示P的转置 使用方法：[P，G]=Gaussian(H,x)，x=1 or 2,1表示G的左边为单位阵
上传时间： 2014-11-27
上传用户：semi1981
这是一个非常简单的遗传算法源代码，代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的 文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。
上传时间： 2015-10-16
上传用户：曹云鹏
基于libsvm，开发的支持向量机图形界面(初级水平)应用程序，并提供了关于C和sigma的新的参数选择方法，使得SVM的使用更加简单直观.参考文章 Fast and Efficient Strategies for Model Selection of Gaussian Support Vector Machine 可google之。
标签： libsvm
上传时间： 2015-10-16
上传用户：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.
标签： sequential simulation posterior overview
上传时间： 2015-12-31
上传用户：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.
标签： monitoring sequential industria accurate
上传时间： 2013-12-17
上传用户：familiarsmile
用于产生gamma分布的噪声序列，以及分析gaussian噪声的各参数。
上传时间： 2016-01-08
上传用户：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.
标签： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上传时间： 2016-01-23
上传用户：569342831
