Explaining the intuition behind Bayesian inference

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Explains how changes to the prior and data (acting through the likelihood) affect the posterior.
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...

Опубликовано:

11 сен 2024

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Комментарии : 14

@ngorovitch 5 лет назад

You should probably do a seminar to our professors on how to teach this. I'm a graduate student in Data Science and it took me 2 semesters to pass the bayesian statistics class. Stopped going to class because I couldn't understand anything, watched the series from this video, went to exams, and passed it with an A-. So thank you. This is the simplest way to understand Bayesian Statistics and very helpful even at the graduate level.

@thijsgelton 3 года назад

I fully agree. My teacher does this bottom up approach where he dives deep into the mathematics without any intuition/examples/concepts or whatever.

@hastybeaver Год назад

This.

@drfaust206 5 лет назад

This is an absolutely fantastic demonstration. Thanks for this! You can't beat a great visualization in making stats things like this crystal clear!

@zaiyuanlu8751 3 года назад

It is so amusing to watch this video, everything seem clear and intuitive. You are a true hero

@lexparsimoniae2107 4 года назад

What a marvellous presentation! Thank you Ben.

@wahabfiles6260 4 года назад

You videos are so so informative, I wish I could subscribe twice!

@user-ws3jn2jw2f 6 месяцев назад

so useful

@donlee6739 5 лет назад

Great explanation, but should the X axis for likelihood be from 0 to 10, instead of 0 to 100, same as for prior and posterior?

@akyol9045 4 месяца назад

As far as I understand, the likelihood distribution is just the distribution of probability densities of x=2 over all possible parameters, which is p for binomial distribution. It is maxed at 0.2, since MLE of the binomial distribution is simply p. You probably manage to understood long ago, but some might find my explanation useful.