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I wanted to know some difference between these two "System Simulation" technical points.

(It would mean a lot. Thanks ahead. I can't find it anywhere, so.) (Let me know if u need more info)

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closed as too broad by drhab, Kavi Rama Murthy, Saad, Eevee Trainer, Theo Bendit Apr 29 at 3:12

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ This question is IMV too broad. $\endgroup$ – drhab Apr 14 at 11:49
  • $\begingroup$ @drhab what IMV stand for? I google it, one was "in my view". But if that was right guess, than u have a grammar mistake. I should be, "IMV this question is too broad" $\endgroup$ – Prabesh Bhattarai Apr 15 at 3:35
  • $\begingroup$ Indeed "in my view" is what I had in mind. Thank you for attending me on my grammar mistake. Without thinking I used an order that is okay in Dutch language but wrong in English. $\endgroup$ – drhab Apr 15 at 6:31
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I think you want to know the difference between Probability Mass Function(pmf) and Probability Density Function(pdf).

Probability Mass Function is for the probability distribution of a discrete random number.

PDF vs PMF

In Simulation, we need to generate random numbers according to the probability distribution. The basic random number generator usually generates a uniform random number between 0 and 1.

When the random number is discrete, we divide the range from 0 to 1 in the way that each interval becomes the probability.
For example, rolling a die, each number has a chance of $\frac{1}{6}$
Therefore, if the uniform random number fall in between 0 and $\frac{1}{6}$, generate a random number 1, if it falls in between $\frac{1}{6}$ and $\frac{2}{6}$, generate 2, and so on.

However, continuous random number case, we cannot divide the range.
Therefore, we match the uniform random number to the CDF (Cumulative Density Function)

the details can be found here. Simulation class Note

Note, the discrete case is also matching uniform to CDF technically.

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  • $\begingroup$ But my exam paper has asked, "Discrete probability function Vs Probability density function". Is the question is wrong? Why can't be there some difference between them? $\endgroup$ – Prabesh Bhattarai Apr 15 at 3:39
  • $\begingroup$ Probability Mass Function is a function shows the probability of a discrete random number. PMF is more widely/technically used, but I would not say the Discrete Probability Function is a wrong expression. $\endgroup$ – S. Phil Kim Apr 15 at 3:46
  • $\begingroup$ What should I do? My university question bank includes "Discrete probability function Vs Probability density function". What should I do in that case? Should I correct the question and write this answer! $\endgroup$ – Prabesh Bhattarai Apr 15 at 3:52
  • $\begingroup$ I meant that you can think Discrete Probability Function = Probability Density Function. $\endgroup$ – S. Phil Kim Apr 15 at 3:54

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