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用蒙特卡洛法仿真掷骰子问题，可以通过增加实验次数来无限逼近理论概率-Dice with the Monte Carlo simulation of the problem, you can experiment by increasing the number of times the probability of an infinite approximation theory
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本程序和文件主要用蒙特卡洛的思想来仿真骰子投掷事件发生的概率。用增加投掷次数的方法来逼近实际。-This procedure, and the paper s main ideas to use the Monte Carlo simulation of the probability of dice throwing incident. With the increase in the number of ways to throw close to the actual.
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基于部分相关的捕获程序，并且通过蒙特卡罗仿真计算检测概率。-Based on the relevant part of the capture process, and by Monte Carlo simulation of probability of detection.
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本程序研究在单个脉冲发现概率一定的条件下，对目标辐射源发现概率随时间的变化。
％采用蒙特卡罗过程仿真方法；-This procedure study found that the probability of a single pulse under certain conditions, sources on the target discovery probability changes over time. Monte Carlo simulation methods
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碰撞检测（用蒙特卡洛法对目标命中概率进行仿真分析）-Collision detection (using Monte Carlo simulation on the target hit probability analysis)
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用monte carlo的方法对Nakagami的概率密度函数进行仿真-Using monte carlo methods for Nakagami probability density function of the simulation
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蒙特卡罗模拟随机数是最好的方式，本程序设计在552nm脉冲后，再一左一右的657nm冷却脉冲，测试激发达到100 的概率，尤其考查蓝、绿及近红外波段-MATLAB 6.5 Monte Carlo simulation cool3.m
This simulation cycles "num" number of atoms "steps" times, with a cooling sequence of
a 657 nm cooling pulse from the right, the
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monte carlo simulation estimate and error probability ploting for a binary communication system employing matched filter
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运行一个Matlab程序，完成一个M＝4的PAM通信系统的仿真。仿真对10000个符号（2万个比特）进行。测量在噪声方差为0,0.1,1.0和2.0时的符号差错概率。通过低通滤波器。画出理论误码率和由Monte Carlo仿真测得的误码率，并比较这些结果。(平均符号能量为1)-Running a Matlab program, complete a M = 4 PAM communication system simulation. The simulation of 10000 symbols
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This report describes the application of Monte Carlo Simulation to solve a simple problem which is to find area under the curve. Monte Carlo methods are often used in simulating physical and mathematical systems. The idea of using Monte Carlo method
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使用m文件产生加噪声的射频信号，通过滤波得到中频信号和基带信号，然后对其取倒谱。经过门特卡罗仿真，采用统计方式，得到各点的概率分布律，然后计算结果的香农熵和互雷尼信息熵。-M files generated using the RF signal plus noise, obtained by filtering the IF signal and baseband signal, and then take its cepstrum. After the door Monte Carlo si
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首先建立了风力发电和光伏发电的天气模型，提出了衡量供电可靠性的失负荷概率LOLP、衡量清洁能源浪费的失能量概率LOEP 和系统成本等指标，并采用蒙特卡罗仿真进行计算.-First set up the model of wind power and photovoltaic power, put forward the measure of the loss of load probability of power supply reliability LOLP LOEP, measure th
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风电场的大规模接入使得电力系统在进行规划设计和
方式安排时需要计及风电出力的不确定性，概率潮流计算方
法是在计及不确定因素的条件下分析电力系统运行状态的
重要工具。针对当前所使用的概率潮流计算方法的不足，提
出一种可以处理多个输入随机变量相关性的基于拉丁超立
方采样(Latin hypercube sampling，LHS)的Monte Carlo 模拟
概率潮流计算方法，该方法同时还具有精度高和速度快等优
点，并且不受输入随机变量的概率分布类型的约束。-Access
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用monte carlo仿真估计并画出利用反极性信号的二进制通信系统的差错概率性能。-Using Monte Carlo simulation and draw the error probability performance by using the binary communication system reverse polarity signal..
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本程序是mazen.O的经典论文：Performance Analysis of two-hop ralayed transmissions over rayleigh fading channels 的文章中所附的仿真图的程序，跑出的结果和文章中的附图是一样的！可以跑出里面的三张图，包括瑞利衰落下中继信道的中断概率的蒙特卡洛及公式的仿真，及选择不同的增益的性能差别曲线！
-This program is mazen.O classic paper: Performance Analysis
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a Matlab code that produces a plot of probability of bit error rate for QPSK versus SNR for three receive antennas, and three different combining methods: MRC, equal gain combining, and selection combining. The plot is generated by using Monte Carlo
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a Matlab code that produces a plot of probability of bit error rate for QPSK versus SNR for three receive antennas, and three different combining methods: MRC, equal gain combining, and selection combining.generate the plot using Monte Carlo simulati
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The Monte Carlo technique is a flexible method for simulating light propagation in tissue. The simulation is based on the random walks that photons make as they travel through tissue, which are chosen by statistically sampling the probability distrib
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蒙特卡罗仿真程序,包括硬币投掷、古典概率、几何概率、Buffon实验。-Monte Carlo simulation program, including coin throwing, classical probability, geometric probability, Buffon experiment.
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Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models.
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