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-- JorgeRodriguez - 2012-01-09 |
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This lab is to be completed mostly on a computer. You can use the python tools you installed on your PC or Mac. In this lab there are four related parts all designed to introduce you to Monte Carlo techniques, essential tools in the analysis of data in all modern scientific endeavors, build familiarity with the three basic statistical distributions important in data analysis, and to further your computing skills.
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- Part A: Here you will first employ the Monte Carlo acceptance/rejection method to compute the value of π and its statistical uncertainty. You may use any programming language you want as long as it is C, C++, Python, MATLAB, or FORTRAN. Since all have deployed an IDE and have been encouraged to use it to develop python scripts and Dr. Boeglin's excellent set of python wrapper tools in his LabTools3 I encourage you to continue to use that for this and all of the computing projects assigned in this class.
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- Part A: Here you will first employ the Monte Carlo acceptance/rejection method to compute the value of π and its statistical uncertainty. You may use any programming language you want as long as it is C, C++, Python, MATLAB, or FORTRAN. Since all have deployed an IDE and have been encouraged to use it to develop python scripts and Dr. Boeglin's excellent set of python wrapper tools in his LabTools3 encourage you to continue to use that for this and all of the computing projects assigned in this class.
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- Part B: Here you will apply what you learned to compute the volume of an n-dimensional sphere, and its error, essentially compute integrals using the MC acceptance/rejection method.
- Part C: Here the goal is to generate data drawn from normal or Gaussian distribution. You then histogram the data and fit the histogram to a Gaussian probability distribution function and discuss the results.
- Part D: Now you will use the transformation method to now generate data drawn from a Poisson distribution. As above you will histogram the data but do not need to do a fit.
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