The discussion revolves around the interpretation of the symbol Pi (##\pi##) in the context of discrete random variables within probability theory, specifically related to Bernoulli, Binomial, and Poisson distributions. Participants are exploring its meaning and implications in various equations provided.
Several participants have provided insights into the role of Pi in different distributions, noting that it can represent the probability of success or other parameters. Questions remain about the specific interpretation of P(Y=y|Pi) and its implications for discrete random variables.
Participants are working with equations related to discrete random variables, and there is an emphasis on understanding the definitions and roles of parameters in various probability distributions. The original poster has repeated their question, indicating ongoing uncertainty.
It looks like ##\pi## just represents the parameter of the distribution. So this is not the common definition ##\pi = 3.14##... In the Bernoulli and Binomial cases, ##\pi## is the probability of success, and in the Poisson example, ##\pi## is the average rate of occurrence.zak100 said:
zak100 said:
It's the probability that a discrete random variable Y (with an assumed distribution) has the value y, given that the parameter of the distribution is ##\pi##.zak100 said:
