You Know I-m All About that Bayes: Crash Course Statistics #24
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Date: 2022-04-04
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Comments and reviews: 10
MadaxeMunkeee
I-m sure the host is a very skilled statistician, but it would have been nice to have more help on the script writing from a Bayesian. I don-t feel like this was a fair representation of bayesian statistics, and might even turn people off who might otherwise find it more convincing.
The video wasn-t wrong, it just focuses on the wrong things and in so doing misses out on some important points. Especially uncertainty, but also quite a few other things.
But as injustices in the world go, this is a pretty minor gripe.
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I-m sure the host is a very skilled statistician, but it would have been nice to have more help on the script writing from a Bayesian. I don-t feel like this was a fair representation of bayesian statistics, and might even turn people off who might otherwise find it more convincing.
The video wasn-t wrong, it just focuses on the wrong things and in so doing misses out on some important points. Especially uncertainty, but also quite a few other things.
But as injustices in the world go, this is a pretty minor gripe.
reply
Jon
It seems like the way to objectively analyse a thing would be to multiply the likelihood-ratios of all credible studies together. This would be the same as iteratively doing Bayes analysis on each study, constantly updating your prior, starting with the assumption that the thing is as likely as not to be true. I would argue that while not always useful (bias is sometimes the result of a lifetime of non-scientific experimentation and is not always worthless) this is the strictest definition of not having a bias.
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It seems like the way to objectively analyse a thing would be to multiply the likelihood-ratios of all credible studies together. This would be the same as iteratively doing Bayes analysis on each study, constantly updating your prior, starting with the assumption that the thing is as likely as not to be true. I would argue that while not always useful (bias is sometimes the result of a lifetime of non-scientific experimentation and is not always worthless) this is the strictest definition of not having a bias.
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Les
It does not need to have subjective figure#. Ie when used as part of a diagnostic decision tree the prior probability can take the form of prevalence within a given populations or sample as determined by a gold standard test group which is then updated by the predictive power of a given test, the likelihood of that test being positive and being correct over a positive being incorrect compare with the same for true and false negative
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It does not need to have subjective figure#. Ie when used as part of a diagnostic decision tree the prior probability can take the form of prevalence within a given populations or sample as determined by a gold standard test group which is then updated by the predictive power of a given test, the likelihood of that test being positive and being correct over a positive being incorrect compare with the same for true and false negative
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Angel
I am not a fan of subjectivity in mathematics. Especially statistical data, as it negates the flawless nature of pure numbers that give statistics their inherently irrefutable significance. I was actually going to take Bayesian Inference next semester but may choose not to now. One might say I have updated my prior belie. Cheese and crackers! You've won this round Mister Doctor Bayes.
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I am not a fan of subjectivity in mathematics. Especially statistical data, as it negates the flawless nature of pure numbers that give statistics their inherently irrefutable significance. I was actually going to take Bayesian Inference next semester but may choose not to now. One might say I have updated my prior belie. Cheese and crackers! You've won this round Mister Doctor Bayes.
reply
JaneFord
Hi, thanks for the video. What I wonder is, what are - default priors- when it comes to bayesian inference? As I understand, the priors are specific to each hypothesis or data, so how come some packages include these defaults? What do these priors entail?
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Hi, thanks for the video. What I wonder is, what are - default priors- when it comes to bayesian inference? As I understand, the priors are specific to each hypothesis or data, so how come some packages include these defaults? What do these priors entail?
reply
Nauman
After watching more then 20 videos and reading many articles related to -Bayesian Statistics-
This video cleared my concept in a very easy way
Thank you so much for sharing great video
Now my prior belief about BAYESIAN has been updated
reply
After watching more then 20 videos and reading many articles related to -Bayesian Statistics-
This video cleared my concept in a very easy way
Thank you so much for sharing great video
Now my prior belief about BAYESIAN has been updated
reply
Arseny
-2: 23 everything in this formula is incorrect. You don-t write p(0. 001) or p(0. 5, all these numbers must be by themselves. And result is 0. 0079, meaning less than 1% chance that it is a male, not 79% as you wrote.
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-2: 23 everything in this formula is incorrect. You don-t write p(0. 001) or p(0. 5, all these numbers must be by themselves. And result is 0. 0079, meaning less than 1% chance that it is a male, not 79% as you wrote.
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Taha
This video, including the animations and graphics, nicely breaks a lot of stereotypes, apart from the stereotype of scientists necessarily like Starwars (or even know anything/care about it!
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This video, including the animations and graphics, nicely breaks a lot of stereotypes, apart from the stereotype of scientists necessarily like Starwars (or even know anything/care about it!
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-lvaro
Legend has it Maria is still on that cafe. She didn't say a word to her date because he was stuck on her head doing math. She hasn't spoken nor moved ever since
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Legend has it Maria is still on that cafe. She didn't say a word to her date because he was stuck on her head doing math. She hasn't spoken nor moved ever since
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weksauce
You forgot the most important pitfall: fanatics with 0% or 100% prior beliefs can never escape them, no matter how convincing the evidence you present.
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You forgot the most important pitfall: fanatics with 0% or 100% prior beliefs can never escape them, no matter how convincing the evidence you present.
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