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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: 3249 |
Author: 西晃云 |
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Description: 蒙特卡罗 课件ppt 好东西哦
monte carlo方法pdf-Monte Carlo courseware ppt Oh good monte carlo methods pdf
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Size: 251904 |
Author: wujianjun |
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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.
Platform: |
Size: 3072 |
Author: 西晃云 |
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Description: Construction of effective potentials for monoatomic systems from
* given RDF
*
* The program performs one iteration of the inverse
* Monte Carlo scheme, which was originally suggested in:
* A.P.Lyubartsev, A.Laaksonen, Phys.Rev.E, v.52(4) 3730 (1995)
*
* Relevant reference is also:
* A.P.Lyubartesv, A.Laaksonen, Comp.Phys.Comm,v.121-122, 57 (1999)-Construction of effective potentials for monoatomic systems from
* given RDF
*
* The program performs one iteration of the inverse
* Monte Carlo scheme, which was originally suggested in:
* A.P.Lyubartsev, A.Laaksonen, Phys.Rev.E, v.52(4) 3730 (1995)
*
* Relevant reference is also:
* A.P.Lyubartesv, A.Laaksonen, Comp.Phys.Comm,v.121-122, 57 (1999)
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Size: 7168 |
Author: 张坤 |
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Description: （蒙特卡洛方法）
求出四分之一圆占正方形的比例。随机投点P(x,y)于正方形，π/4的近似值为落在四分之一圆内的点数与总投点数的比值。随机投点次数分别为10000次和50000次，计算π的近似值。
-(Monte Carlo method) are obtained accounting for a quarter of a round square ratio. Voted random point P (x, y) in the square, π/4 for the fall quarter of the approximation of the points won and the ratio of the total vote points. The number of random points, respectively, voted for the 10000 and 50000 times, the calculation of the approximation of π.
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Size: 8192 |
Author: mazhen |
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Description: Monte Carlo localization
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Size: 7168 |
Author: ovidel |
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Description: 两种不同的假设：
H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率
H0 ： xn wn () () = n=1,2,…,N
其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互
独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布
1 0 2 ( ) 20 p
θ π θ π ?? ≤ ≤ = ??? 其它
1）改变输入信噪比（改变 A 或噪声方差均可），给定虚警概率，画出
输入信噪比与检测概率之间的理论曲线。(注意：理论检测曲线与样本
数有关) 2）改变样本数，用 Monte-Carlo 实验方法得出 PF＝0.001 时输入信噪比
与检测概率之间关系曲线（至少三条），并得出结论。 3）改变 M-C 实验次数，样本数不变，用 Monte-Carlo 实验方法得出 PF ＝0.001 时输入信噪比与检测概率之间关系曲线（至少三条），并得出
结论。(Draw the theoretical curve between input SNR and detection probability)
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Size: 3072 |
Author: 原野5266 |
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Description: 两种不同的假设：
H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率
H0 ： xn wn () () = n=1,2,…,N
其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互
独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布
1 0 2 ( ) 20 p
θ π θ π ?? ≤ ≤ = ??? 其它
1）改变输入信噪比（改变 A 或噪声方差均可），给定虚警概率，画出
输入信噪比与检测概率之间的理论曲线。(注意：理论检测曲线与样本
数有关) 2）改变样本数，用 Monte-Carlo 实验方法得出 PF＝0.001 时输入信噪比
与检测概率之间关系曲线（至少三条），并得出结论。 3）改变 M-C 实验次数，样本数不变，用 Monte-Carlo 实验方法得出 PF ＝0.001 时输入信噪比与检测概率之间关系曲线（至少三条），并得出
结论(Draw the theoretical curve between input SNR and detection probability)
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Size: 2048 |
Author: 原野526 |
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