Niels Burghoorn pointed out a mistake here (thanks Niels!) at about 4 mins -- I say "Poisson prior", what I meant to say was "Poisson likelihood". Sorry for any confusion caused! Best, Ben
Just a brief question. Everyone is talking about "sampling" from the posterior. Isn’t it really more like "simulating a posterior"? How can you sample from something unknownˋ
Thanks a lot for this video. I've been trying to intuitively pin down posterior predictive distribution for a while now, and within the first two minutes, your explanation of it as an approximation of the posterior distribution on new data, along with the two-step iterative process, finally made it click.
Thank you very much for combining your book ("A Students Guide to Bayesian Statistics") with these lectures! 1) Perhaps I missed it, and perhaps it is a naive question, but how does this work in the context of a data generating process that produces a "thinner" distribution than the actual data? 2) Could one conceivably "p-hack" this form of the statistic (e.g., generate a small number of PPC distributions, or use a random seed that is more "favorable" to a desired hypotheses)? I'm just trying to figure out some of the pros and cons of the "Bayesian p-value", considering the headache it has caused in Frequentist statistics. But perhaps the point was to use the Bayesian p-value as an example of a kind of PPC?
Niels Burghoorn pointed out a mistake here (thanks Niels!) at about 4 mins -- I say "Poisson prior", what I meant to say was "Poisson likelihood". Sorry for any confusion caused! Best, Ben
Just a brief question. Everyone is talking about "sampling" from the posterior. Isn’t it really more like "simulating a posterior"? How can you sample from something unknownˋ
can u please give us the data and codes from the video
Thanks a lot for this video. I've been trying to intuitively pin down posterior predictive distribution for a while now, and within the first two minutes, your explanation of it as an approximation of the posterior distribution on new data, along with the two-step iterative process, finally made it click.
i never heard this technique used in machine learning to decide if the posterior is good, anyone know why?
could you plz tell me where to find the Matlab codes which are used in these videos and ox-edu comprehensive bayesian stats list videos ?
Thank you very much for combining your book ("A Students Guide to Bayesian Statistics") with these lectures! 1) Perhaps I missed it, and perhaps it is a naive question, but how does this work in the context of a data generating process that produces a "thinner" distribution than the actual data? 2) Could one conceivably "p-hack" this form of the statistic (e.g., generate a small number of PPC distributions, or use a random seed that is more "favorable" to a desired hypotheses)? I'm just trying to figure out some of the pros and cons of the "Bayesian p-value", considering the headache it has caused in Frequentist statistics. But perhaps the point was to use the Bayesian p-value as an example of a kind of PPC?
Great video! Thank you
Thanks! That was a great explanation!!
You are awsome awsome teacher!!
sir, u truly saved me😅