An introduction to the Bernoulli and binomial distributions
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- Опубліковано 29 вер 2024
- This video provides introductions to the Bernoulli and Binomial distributions, and explains the circumstances when we may choose to use these.
This video is part of a lecture course which closely follows the material covered in the book, "A Student's Guide to Bayesian Statistics", published by Sage, which is available to order on Amazon here: www.amazon.co....
For more information on all things Bayesian, have a look at: ben-lambert.co.... The playlist for the lecture course is here: • A Student's Guide to B...
I just wanna say I have watched so many of your videos, and I want you to know that you are helping me out not only to pass my exams, but to actually understand the math! Thank you! I wish my professor was like you.
Hi Ben, would you agree with the following statement? A Bernoulli distribution is a type of binomial distribution, but where the number of trials is limited to 1. I found this on wikipedia and it was a very helpful statement to distill what appears to be the key point in categorising a Bernoulli distribution as a type of binomial distribution with an added limitation around num of trials. Thanks a lot
while technically correct, it's not really that there's an "additional restriction" placed on the Bernoulli relative to the Binomial...it's better to focus on the fact that the binomial is a generalization of the single Bernoulli trial to a sequence/repetition of N Bernoulli trials. The outcome of a binomial is the sum of the outcomes of those N Bernoulli trials. This makes it easier to understand the connection between the formulas and the expectations (expectation of Bernoulli is just p, but expectation of Binomial is N*p). A bernoulli is the outcome of a single coin flip (whether you got a H or a T), and a binomial is the outcome of flipping the coin N times and adding up the number of K wins you got out of those N Bernoulli trial flips.
Ben, you're exceptional