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Description: 欲望与理性的挣扎！不断地自欺和伪装，不断地付出与失去她们为了在繁华的都市生存并长期居留，日夜出卖自己甜美柔媚的嗓音，甚至不惜肉体，可最后除了身心疲惫，伤痕累累外，沦为被整个社会鄙视的“罂粟花”…… - Desire and rational struggling! Unceasingly deceives self with
the camouflage, unceasingly pays with loses them for in the lively
metropolis survival and the permanent residence, day and night betrays
the oneself delightful 鏌斿獨 voice, even does not hesitate the human
body, may finally be exhausted except the body and mind, the scar
repeatedly outside, degenerates into by the entire society is despised
\"the poppy flower\"... ...
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E / (kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o
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Size: 9122 |
Author: 刘明 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E / (kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o
Platform: |
Size: 11082 |
Author: 刘明 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E / (kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o
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Size: 6055 |
Author: 刘明 |
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Description: The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use \"help inference\" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use \"help gbp_preprocess\" and \"help gbp\" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use \"help simulatedAnnealing\" from Matlab.
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Size: 83944 |
Author: bevin |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o
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Size: 9216 |
Author: 刘明 |
Hits:
Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o
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Size: 11264 |
Author: 刘明 |
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Description: The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
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Author: bevin |
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Description: this r code for Gibbs sampler and Metropolis sampler which are two variants of markov chain monte carlo simulators.-this is r code for Gibbs sampler and Metropolis sampler which are two variants of markov chain monte carlo simulators.
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Size: 3072 |
Author: meysa |
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Description: MRF example, Ising by Metropolis
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Size: 1024 |
Author: pitypang999 |
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Description: metropolis-Hastings samplermetropolis-Hastings抽样的matlab实现-metropolis-Hastings samplermetropolis-Hastings in matlab
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Size: 1024 |
Author: 谭 |
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Description: Metropolis sampler for Mallows model
samples orderings from a distribution over orderings
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Size: 1024 |
Author: 杨磊 |
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Description: Use Metropolis procedure to sample from Cauchy density
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Size: 1024 |
Author: 杨磊 |
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Description: Adaptive Mixture Modelling Metropolis Methods using matlab
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Size: 1059840 |
Author: seyyed |
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Description: matlab code for metropolis algorithm
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Size: 2048 |
Author: narjis |
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Description: 使用metropolis-hastings抽样方法，产生平稳马尔科夫链，R语言实现-Using sampling methods metropolis-hastings, produce smooth Markov chain, R language
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Size: 4096 |
Author: 农斌 |
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Description: 本文件包含Metropolis算法对函数进行抽样;显示生成样本的相关图和直方图. 其中文件：metropolis_hastings.m该文件包含4个示例，用于通过Metropolis-Hastings算法对复杂函数进行抽样，显示生成样本的相关图和直方图。metropolis_hastings2.m
包含一个例子，用于通过Metropolis-Hastings算法对双变量高斯PDF进行采样，显示生成样本的相关图和直方图，以及其轮廓和边缘PDF的函数等。(This program develops a very basic example, for the sampling of functions by means of Metropolis algorithm; showing the correlograms and the histogram of the generated samples. metropolis_hastings.m. This file contains four examples, for the sampling of complex functions by means of Metropolis-Hastings algorithm, showing the correlograms and the histograms of the generated samples. In this case the proposals PDF its no longer symmetric. Additionally, the burn-in period, the lag period and the Geweke test have been implemented.It needs the "MH_routine.m" function.
metropolis_hastings2.m. This file contains one example, for the sampling of a bivariate Gaussian PDF by means of Metropolis-Hastings algorithm, showing the correlograms and the histograms of the generated samples, and the function with its contours and marginals PDF. Additionally, the burn-in period, the lag period and the Geweke test have been implemented.)
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Size: 11264 |
Author: 3Radiant
|
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Description: Metropolis Hastings code
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Size: 31744 |
Author: Dan.act |
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Description: Ising模型的蒙特卡罗模拟.模拟能量及磁矩随温度变化(Ising Model And Metropolis Algorithm. Copyright 2017 The MathWorks, Inc.)
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Size: 4968448 |
Author: mokangxin |
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Description: 一种用于对各类概率密度函数进行样本采样的Metropolis-Hastings算法(a Metropolis-Hastings algorithm for sampling from various probability density functions)
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Size: 1024 |
Author: daoguangdong |
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