# a set of 2 questions: what is the scenario in which rolling dice could be treated as a multinomial distribution and 2 multinoulli distribution.

This post talks about "the difference between multinomial and categorical distribution" without any concrete examples, and the answer have not been accepted yet.

section 3.9 of the "Deep Learning Book" Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning distinguishes the difference:

The multinoulli distribution is a special case of the multinomial distribution

without any concrete examples either.

It seems that this CMU Machine Learning Course consider rolling dice as multinomial distribution while I think might be multinoulli distribution per the "Deep Learning Book".multinomial

Am I missing something?

in other words, could someone give an concrete example to illustrate the scenario in which rolling dice could be treated as a multinomial distribution and some other scenarios in which rolling dice could be treated as a multinoulli distribution

• Your question is hard to follow...I think you are jumbling your terms. Please edit for clarity.
– lulu
Commented Nov 9, 2019 at 12:06
• @lulu I've updated my question, would you please take a loot at that? Commented Nov 9, 2019 at 12:48
• I think your confusion is purely semantic. For a two state outcome, a Bernoulli process is the same as a one trial Binomial process. Same thing here. One roll of a die is either a multinoulli process or a one trial multinomial process.
– lulu
Commented Nov 9, 2019 at 12:50
• @lulu thanks for your reply. in this context, shall I treat 2 rolls of a die a 2 trial multinomial process which is not a multinoulli process, right? Commented Nov 9, 2019 at 12:54
• Just think about the two state case. A two trial binomial process is not the same as a bernoulli process. But...don't get hung up on vocabulary. People have given names to some standard distributions that seem to arise a lot, but the important thing is the underlying distribution...not the name we give to it.
– lulu
Commented Nov 9, 2019 at 12:56