Description: ReBEL is a Matlabtoolkit of functions and scripts, designed to
facilitate sequential Bayesian inference (estimation) in general state
space models. This software consolidates research on new methods for
recursive Bayesian estimation and Kalman filtering by Rudolph van der
Merwe and Eric A. Wan. The code is developed and maintained by Rudolph
van der Merwe at the OGI School of Science & Engineering at OHSU
(Oregon Health & Science University).
-ReBEL is a Matlabtoolkit of functions and s cripts. designed to facilitate in sequential Bayesian ference (estimation) in general state space mo dels. This software consolidates research on n ew methods for Bayesian estimation a recursive nd Kalman filtering by Rudolph and van der Merwe Eric A. Wan. The code is developed and maintaine d by Rudolph van der Merwe at the OGI School of Sci ence Platform: |
Size: 489472 |
Author:薛斌 |
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Description: ％ PIEFLAB Main Directory
％ ----------------------
％
％ .m - files
％ ----------
％ Contents.m : this file
％ startup.m : startup file: sets Matlab path executed automatically when
％ Matlab command is performed in this directory
％
％ subdirectories
％ --------------
％ General/ : general matlab commands
％ MRF/ : Markov Random Field: Bayesian algorithm for images
％ Noise/ : noise generation, density/distribution functions
％ Tests/
％ Threshold/ : threshold procedures (includes threshold assessment)
％ WT/ : Wavelet Transform routines
％ Poisson/ : Poisson intensity estimation routines
％ Copyright (c) Maarten Jansen
％ K.U.Leuven - Flanders (Belgium)
％
％ This software is part of PiefLab and is copyrighted material. More info on
％ copyright policy is available on:
％ www.cs.kuleuven.ac.be/~maarten/software/- PIEFLAB Main Directory----------------------. M- files---------- Contents.m: this file startup.m: startup file: sets Matlab path executed automatically when Matlab command is performed in this directory subdirectories-------------- General /: general matlab commands MRF /: Markov Random Field: Bayesian algorithm for images Noise /: noise generation, density/distribution functions Tests/Threshold /: threshold procedures (includes threshold assessment) WT /: Wavelet Transform routines Poisson /: Poisson intensity estimation routines Copyright (c) Maarten Jansen KULeuven- Flanders (Belgium) This software is part of PiefLab and is copyrighted material. More info on copyright policy is available on: www.cs.kuleuven.ac.be/ ~ maarten/software / Platform: |
Size: 113664 |
Author:汪伟 |
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Description: On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
Platform: |
Size: 16384 |
Author:徐剑 |
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Description: sbgcop: Semiparametric Bayesian Gaussian copula estimation
This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data.
Version: 0.95
Date: 2007-03-09
Author: Peter Hoff
Maintainer: Peter Hoff <hoff at stat.washington.edu>
License: GPL Version 2 or later
URL: http://www.stat.washington.edu/hoff
CRAN checks: sbgcop results
Downloads:
Package source: sbgcop_0.95.tar.gz
MacOS X binary: sbgcop_0.95.tgz
Windows binary: sbgcop_0.95.zip
Reference manual: sbgcop.pdf Platform: |
Size: 5120 |
Author:cy |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. Platform: |
Size: 220160 |
Author:晨间 |
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Description: 详细介绍MCL算法，是由Sebastian Thrun a, Dieter Fox, Wolfram Burgard, Frank Dellaert所著的论文，发表于Artificial Intelligence上。-Mobile robot localization is the problem of determining a robot’s pose from sensor data. This
article presents a family of probabilistic localization algorithms known as Monte Carlo Localization
[MCL]. MCL algorithms represent a robot’s belief by a set of weighted hypotheses [samples],
which approximate the posterior under a common Bayesian formulation of the localization problem.
Building on the basic MCL algorithm, this article develops a more robust algorithm called Mixture-
MCL, which integrates two complimentary ways of generating samples in the estimation. To apply
this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that
permits fast sampling. Systematic empirical results illustrate the robustness and computational
efficiency of the approach. 2001 Published by Elsevier Science B.V.
Keywords: Mobile robots Localization Position estimation Particle filters Kernel density trees Platform: |
Size: 1425408 |
Author:xuyuhua |
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Description: 贝叶斯估计方法的matlab程序,这是一个简单的例子,但很有用-Bayesian estimation methods matlab program, this is a simple example, but very useful Platform: |
Size: 1024 |
Author:李凌 |
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Description: 1）最大似然方法联合实现符号定时同步和载波同步仿真
2）泊松分布
3）贝叶斯估计
4）RANSAC方法-1) The maximum likelihood method of the joint realization of Symbol Timing and Carrier Synchronization in simulation 2) Poisson distribution 3) Bayesian estimation 4) RANSAC method Platform: |
Size: 4096 |
Author:平凡 |
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Description: 通过单一的Wi-Fi接入点的信号强度来判断移动物体的位置。比较新的一篇文章。用了蒙特卡罗抽样的办法－Monte Carlo Sampling Method－来估计位置。-This paper describes research towards a system
for locating wireless nodes in a home environment requiring
merely a single access point. The only sensor reading used for
the location estimation is the received signal strength indication
(RSSI) as given by an RF interface, e.g.,Wi-Fi.Wireless
signal strengthmaps for the positioning filter are obtained by
a two-step parametric and measurement driven ray-tracing
approach to account for absorption and reflection characteristics
of various obstacles. Location estimates are then
computed using Bayesian filtering on sample sets derived
by Monte Carlo sampling. We outline the research leading
to the system and provide location performance metrics using
trace-driven simulations and real-life experiments. Our
results and real-life walk-troughs indicate that RSSI readings
from a single access point in an indoor environment
are sufficient to derive good location estimates of users with
sub-room precision. Platform: |
Size: 452608 |
Author:weihuagao |
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Description: 用监督参数估计中的贝叶斯方法估计条件概率密度的参数u-With the supervision of the Bayesian estimation method to estimate the parameters of the conditional probability density of u Platform: |
Size: 1024 |
Author:yan |
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Description: ReBEL is a Matlab® toolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University-ReBEL is a Matlab® toolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University Platform: |
Size: 1703936 |
Author:hoanglaota |
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Description: 贝叶斯估计 和极大似然估计的matlab程序-matlab program of Bayesian estimation and maximum likelihood estimation Platform: |
Size: 343040 |
Author:luxh |
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Description: 实验要求：
1. 以身高为例，画出男女生身高的直方图并做对比；
2. 采用最大似然估计方法，求男女生身高以及体重分布的参数；
3. 采用贝叶斯估计方法，求男女生身高以及体重分布的参数（假定方差已知，作业请注明自己选定的一些参数情况）；
4. 采用最小错误率贝叶斯决策，画出类别判定的决策面。并判断某样本的身高体重分别为(160,45)时应该属于男生还是女生？为(178,70)时呢？(Experimental requirements:
1. take height as an example, draw the height histogram of male and female students, and make a comparison;
2. the maximum likelihood estimation method was used to calculate the height and weight distribution parameters of male and female students;
3. Bayesian estimation method is used to find the parameters of height and weight distribution of male and female students (assuming variance is known, please indicate some parameters selected by homework);
4., the Bayes decision with minimum error rate is used to draw the decision surface of category decision. And determine the height and weight of a sample respectively (160,45), should belong to the boys or girls? What about (178,70)?) Platform: |
Size: 237568 |
Author:PPLL
|
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