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Description: mcmc 马尔可夫链 蒙特卡罗算法
具体参数 请用help命令-MCMC Markov chain Monte Carlo algorithm specific parameters Please help command
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Description: Computes BER v EbNo curve for convolutional encoding / soft decision
Viterbi decoding scheme assuming BPSK.
Brute force Monte Carlo approach is unsatisfactory (takes too long)
to find the BER curve.
The computation uses a quasi-analytic (QA) technique that relies on the
estimation (approximate one) of the information-bits Weight Enumerating
Function (WEF) using
A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.-Computes BER v EbNo curve for convolutional encoding/soft decision Viterbi decoding scheme assuming BPSK. Brute force Monte Carlo approach is unsatisfactory (takes too long) to find the BER curve.The computation uses a quasi-analytic (QA) technique that relies on theestimation (approximate one) of the information-bits Weight Enumerating Function (WEF) usingA simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
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Size: 6144 |
Author: joy |
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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.
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Size: 16384 |
Author: 徐剑 |
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Description: 这是monte carlo粒子滤波的一个实例程序，对于学习卡尔曼滤波和粒子滤波都有很大帮助-This monte carlo particle filter is an example of procedures for the study of Kalman filtering and particle filter are very helpful
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Size: 8192 |
Author: ZhangGeng |
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Description: 里面有蒙特卡洛的基本课间，资源简易易懂。-There are the basic Monte Carlo recess, resources simple and understandable.
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Size: 1101824 |
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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.
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Size: 220160 |
Author: 晨间 |
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Description: monte carlo 仿真英文电子书
AGuidetoMonteCarloSimulationsinStatisticalPhysics,Second EditionThis new and updated deals with all aspects of Monte Carlo simulation ofcomplexphysicalsystemsencounteredincondensed-matterphysicsandsta-tistical mechanics as well as in related ?elds, for example polymer science,lattice gauge theory and protein folding-monte carlo simulation English e-books AGuidetoMonteCarloSimulationsinStatisticalPhysics, Second EditionThis new and updated deals with all aspects of Monte Carlo simulation ofcomplexphysicalsystemsencounteredincondensed-matterphysicsandsta-tistical mechanics as well as in related? elds, for example polymer science, lattice gauge theory and protein folding
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Author: 林峰 |
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Description: 蒙特卡罗方法可以有效地解决复杂的工程问题，而MATLAB具有强大的数值计算功能。将二者结合使用，可以得到高效的解决问题的方法。本文阐述了该方法并通过实例验证了该方法的正确性和优越性。-Monte Carlo method can effectively solve complex engineering problems, and MATLAB has powerful function of numerical calculation. The two in combination, can be cost-effective solution to the problem. This paper describes the methods and examples of the method to verify the correctness and superiority.
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Size: 120832 |
Author: zzh |
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Description: 这是马尔可夫-蒙特卡罗算法的MATLAB源程序.-This is the Markov- Monte Carlo algorithm for MATLAB source code.
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Size: 136192 |
Author: liufanmao |
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Description: 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.
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Size: 3072 |
Author: 西晃云 |
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Description: 用matlab对M=8的PSK系统进行蒙特卡罗仿真，分析其误码率-Using matlab for M = 8 the PSK system Monte Carlo simulation, to analyze the bit error rate
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Size: 3072 |
Author: wuyinkui |
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Description: MCMC（马尔可夫－盟特卡罗方法）实现的程序-MCMC (Markov- UNITA Monte Carlo method) procedures realize
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Size: 32768 |
Author: chenzhuo |
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Description: pMatlab is a toolsbox from MIT for running matlab in parallel style on a multi-core PC or a cluster environment. These two documents summary the usage of pMatlab and running time measurements on three simple Monte Carlo simulation codes.
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Author: wei yu |
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Description: 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.-Sequential Monte Carlo without Likelihoods Particle Filtering likelihood function do not have the circumstances of this article Abstract: Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributionsin the presence of analytically or computationally intractable likelihood functions.Despite representing a substantial methodological advance, existing methods based on rejectionsampling or Markov chain Monte Carlo can be highly inefficient, and accordinglyrequire far more iterations than may be practical to implement. Here we propose a sequentialMonte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrateits implementation through an epidemiological study of the transmission rate of tuberculosis .
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Size: 181248 |
Author: 阳关 |
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Description: 对M=4的PSK通信系统进行蒙特卡罗仿真 简单实用-Of M = 4 of PSK communication systems Monte Carlo simulation of simple and practical
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Author: 李洁 |
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Description: 蒙特卡罗方法完整教程.
蒙特卡罗(Monte Carlo)不同于确定性数值方法，它是用来解决数学和物理问题的非确定性的（概率统计的或随机的）数值方法。Monte Carlo 方法（MCM），也称为统计试验方法，是理论物理学两大主要学科的合并：即随机过程的概率统计理论（用于处理布朗运动或随机游动实验）和位势理论，主要是研究均匀介质的稳定状态。它是用一系列随机数来近似解决问题的一种方法，是通过寻找一个概率统计的相似体并用实验取样过程来获得该相似体的近似解的处理数学问题的一种手段。运用该近似方法所获得的问题的解in spirit更接近于物理实验结果，而不是经典数值计算结果。
关键词: 蒙特卡罗 仿真 模拟打靶 概率
-err
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Size: 130048 |
Author: donotspam |
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Description: very good matlab of Monte carlo
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Size: 232448 |
Author: lee ming |
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Description: 绍了数字通信中的Q PSK 调制解调的原理和过程, 通过用M atlab 对这一过程的编程, 分析信号在
理想信道和加噪信道中模拟传输时的时域图, 并用蒙特卡罗方法, 讨论模拟过程中的误码率, 所得结果与理论
结果基本一致.
关键词:Q PSK 系统仿真 蒙特卡罗分析 M atlab-Shaozeng digital communications Q PSK modulation and demodulation principle and process, M atlab through the process of programming, analysis of signal processing in the ideal channel noise channel analog transmission of time-domain diagram, and Monte Carlo methods discussed in the process of simulation BER results are basically consistent with the theoretical results. Key words: Q PSK System Simulation Monte Carlo Analysis of M atlab
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Size: 1070080 |
Author: zhanghuan |
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Description: This a matlab code that simulate the monte-carlo simulation of using 16-QAM with OFDM system under AWGN case.
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Size: 1024 |
Author: Nick |
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Description: qpsk系统的蒙特卡洛仿真，分别在高斯和瑞丽衰落信道下进行误码率仿真-QPSK system Monte Carlo simulation, respectively, and Ruili in the Gaussian fading channel under BER simulation
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Size: 3072 |
Author: 丁巍 |
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