montecarlo

问题 I have some python program let's say 'Test.py' it has a class and inside this in the init I have to introduce three random variables. import math class Vector(): def __init__(self,vx,vy,vz): self.x=vx self.y=vy self.z=vz def norm(self): xx=self.x**2 yy=self.y**2 zz=self.z**2 return math.sqrt(xx+yy+zz) Now I made a run file 'Testrun.py' which calls this file and then for each dataset it produces one result import math import numpy as np from Desktop import Test def random_range(n, min, max):

问题 I am using a Montecarlo simulation for predicting mpg in the mtcars data. I want to extract the coefficients of all the variables in the dataframe to compute how many times each car has lower mpg than the other car. For example how many times Toyota Corona has less predicted mpg than Datsun 710. This is my initial code using only two independent variables. I want to expand this selection to use all the variables in the data frame without manually have to include all the variables in the data

问题 I am working on an assignment using JAGS to model a binomial distribution who's p parameter is a function of another variable d . This is what I am trying to do: generate 10000 samples from the posterior for the two parameters alpha/beta produce samples to from the posterior predicted number of success when dist = 25 for 100 attempts calculate 95 credible interval for success rate at 25 feet distance I have written the model but it is giving an error. Below is the code I have already tried #R

问题 I am working on an assignment using JAGS to model a binomial distribution who's p parameter is a function of another variable d . This is what I am trying to do: generate 10000 samples from the posterior for the two parameters alpha/beta produce samples to from the posterior predicted number of success when dist = 25 for 100 attempts calculate 95 credible interval for success rate at 25 feet distance I have written the model but it is giving an error. Below is the code I have already tried #R

问题 I am working on an assignment using JAGS to model a binomial distribution who's p parameter is a function of another variable d . This is what I am trying to do: generate 10000 samples from the posterior for the two parameters alpha/beta produce samples to from the posterior predicted number of success when dist = 25 for 100 attempts calculate 95 credible interval for success rate at 25 feet distance I have written the model but it is giving an error. Below is the code I have already tried #R

问题 I am attempting to write a program that executes Monte Carlo simulations using OpenCL. I have run into an issue involving exponentials. When the value of the variable steps becomes large, approximately 20000, the calculation of the exponent fails unexpectedly, and the program quits with "Abort Trap: 6". This seems to be a bizarre error given that steps should not affect memory allocation. I have tried setting normal , alpha , and beta to 0 but this does not resolve the problem however

问题 I am trying to plot a histogram distribution of pi from the Monte Carlo method but I am getting histograms that are either skewed to the left or right each time I run the simulation instead of a histogram that is approximately symmetric and peaks at around 3.14. The output histograms also have some gaps and I think I am approximating pi correctly. My code is below: [...(importing relevant modules)] N = 1000 #total number of random points circlex = [] circley = [] squarex = [] squarey = [] pis

问题 I am trying to run a Monte Carlo for each row of a data frame using R. library(tidyverse) Group.ID <- c('A','B','C') Start.Amount <- c(90.2, 11.7, 37.8) Mean <- c(0.0005106365,0.0006589744,0.000444903) SD <- c(0.01587259,0.02358892,0.02070972) summary <- data.frame(Group.ID, Start.Amount, Mean, SD) For instance I am trying to run a Monte Carlo for Group.ID A then run a different simulation for Group.ID B and so on. I am running a single Group.ID with the following code. #### Monte Carlo

问题 I have a simple game which is based on probabilities, every day we toss a coin and if we get heads then we win and we get $20 and if we toss the coin and we get tails then we lose $19, at the end of the month (28 days) we see how much we have lost or made. def coin_tossing_game(): random_numbers = [random.randint(0, 1) for x in range(500)] #generate 500 random numbers for x in random_numbers: if x == 0: #if we get heads return 20 #we win $20 elif x == 1: #if we get tails return -19 #we lose

问题 I couldn't figure out what's the problem on my code. Basically i'm trying to do a small version of Monte Carlo simulation. This is my input mydata=[1,4,20,23,37] prediction=c(0,0,0,0,0,0,0,0,0,0,0,0) randomvar=runif(12,0,1) and then i have this condition: if i-th value of randomvar is > 0.97, replace i-th value of prediction with 5th data from mydata else if i-th value of randomvar is > 0.93, replace i-th value of prediction with 4th data from mydata else if i-th value of randomvar is > 0.89,