The probability model you are describing is known as a weighted probability model or unequal probability model. In this type of model, each possible outcome has a different probability assigned to it, reflecting that some outcomes are more likely to occur than others.
In a weighted probability model, the total probability must still equal 1, but the individual probabilities can vary. For example, in a simple scenario where you have three outcomes (A, B, and C), the probabilities could be assigned as follows:
- P(A) = 0.5
- P(B) = 0.3
- P(C) = 0.2
Here, outcome A is more likely to occur compared to outcomes B and C. Weighted probability models are widely used in various fields such as statistics, finance, machine learning, and decision-making processes, where different outcomes have varying levels of likelihood due to underlying factors.
Another related concept is probability distributions, such as the Bernoulli distribution, Binomial distribution, or Poisson distribution, where the likelihood of different outcomes can vary based on specific parameters.