oneamp said:-(mu)*ln(1-rand()) is that about right?
oneamp said:rand() is just hypothetical; it generates a 'random' number between 0 and 1.
Since what I've written in the second post is the correct formula for inverting the CDF (Thank you office shredder), is this a correct way that I can generate random numbers according to this distribution?
To generate a random number with a distribution function, you can use a random number generator that allows you to specify the distribution. Some programming languages, such as Python and R, have built-in functions for generating random numbers with various distributions. You can also create your own function using mathematical equations and algorithms.
A distribution function is a mathematical function that describes the probability of a random variable taking on a certain value or falling within a certain range. It is used to model the distribution of a data set and can be used to generate random numbers with similar characteristics.
Yes, you can customize the distribution of your random numbers by choosing a specific distribution function and adjusting the parameters. For example, you can generate numbers with a normal distribution by specifying the mean and standard deviation, or numbers with a uniform distribution by choosing a range of values.
You can check if your random numbers have the desired distribution by visualizing the data in a histogram or by calculating summary statistics such as mean, standard deviation, and skewness. You can also use statistical tests, such as the Kolmogorov-Smirnov test, to determine if the data follows a specific distribution.
Yes, there are limitations to generating random numbers with a distribution function. Some distributions may be more difficult to generate than others, and the quality of the random numbers produced may depend on the algorithm used. Additionally, the distribution may not accurately represent real-world data if the underlying assumptions are not met.