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Sampling from a 1D Distribution Normalize the function f(x) if it isn't already normalized. Integrate the normalized PDF f(x) to compute the CDF, F(x). Invert the function F(x). ... Substitute the value of the uniformly distributed random number U into the inverse normal CDF.

The probability distribution for a random variable describes how the probabilities are distributed over the values of the random variable. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x).

The discrete probability distribution can be represented using a probability mass function (PMF). Continuous Probability Distribution: A continuous probability distribution is a type of probability distribution that deals with random variables that can take on any continuous value within a certain range.

So here's a generic algorithm that might work for you: Generate N random numbers using a distribution of your choosing. Find the sum of all the numbers. Divide each number by its sum. Multiply by the fixed total mass of your object.

Here, P (X) represents the probability of the event X. Thus we can write: P (X) = n/N; where 'n' is the number of the favourable outcomes and 'N' is the number of total possible outcomes.