Bayesian parameter estimation

A Bayesian parameter estimation using a Binomial model as an example.
** TYPO ** The plot of the density shown on slide 8 is incorrect. It should be a Beta(80,42), but instead was a Be(94,84) which was the posterior for Plumlee's free-throw percentage in the previous year.

[@Shnauzzy]

Clearly explained. Thanks Jarad!

[@TimothyChenAllen]

Terrific! I'd seen the theory, but it helps to see it used with real data. I'm getting my MS in Applied Statistics from UDC, and my thesis will be on Bayesian prediction intervals on Poisson processes. This helps a lot. Thanks so much!

[@prometeo34]

Excellent explanation, thanks Jarad

[@frashertseng9426]

How would the 95% HDI is between (57, 74) according do the distribution plot?

[@flavionex]

Very Good mini-lecture! Thanks a lot!

[@drpubuduthilanawl5483]

Very helpful lecture, thanks.

[@CarlosRomeroconnect]

great work!

[@hakiim.jamaluddin]

Please keep posting more bayesian statistics (probably advanced bayesian statistics) videos. Very much helpful for my studies at unsw sydney!

[@jaradniemi]

I might have the opportunity to this fall as I am teaching an advanced Bayesian stats class.

[@hakiim.jamaluddin]

@@jaradniemi thanks!

[@sheilaabukayo5237]

This is an awesome lecture. Thank you

[@duncanstoddard2286]

Great lecture, thanks

[@lightningblade9347]

I don't know if you still make videos but I just want to mention that this video was extremely helpful, bless you man and hope you are doing well.

[@jaradniemi]

I just started making more videos, although perhaps at a more basic level than you need.

[@madushadilshani1897]

Thanks a lot. Very helpful.

[@anwmus]

Thank you very much! May I ask why is the Beta distribution used as the prior - is it just simply for the ease since the posterior will have beta distribution after multiplying the likelihood function (binomial) with the beta?

[@jaradniemi]

Yes, precisely because the posterior will have a beta distribution and thus this prior is "conjugate". You could use a different distribution, but then you would have more mathematical work to do to find the posterior.

[@sjwang3892]

Thanks for creating this content. Really helpful! Would be better if you show Bayesian estimation works with multiple data points :)

[@jaradniemi]

In the binomial model, there are really "n" data points, so this does have multiple data points. Perhaps you want Bayesian parameter estimation in a normal model? ua-cam.com/video/L5pg_cx2nNU/v-deo.html

[@sjwang3892]

@@jaradniemi Thanks for the reply! I meant the examples for continuous distributions. Exactly what you just shared.

[@ashutoshbhushan6107]

Wow, well explained. Sound like you are a professor

[@PedroPwnsYou]

You answered a lot of my questions, thanks

[@choitomines]

Can i have the copy of your presentation?

[@raoufzanati7532]

Thankssssssssssssssss

[@rohanmukherjii]

Can you organize the videos properly with numbers? THe videos are great but hardly organized!

[@jaradniemi]

They're organized here ua-cam.com/play/PLFHD4aOUZFp0Xhzd5j1nWnExD54xJfnJX.html

[@engkareemhamed]

Please I need your email to contact with you My PHD in blind estimation and I need ask you on it