41 - Proof: Gamma prior is conjugate to Poisson likelihood

This video provides a proof of the fact that a Gamma prior distribution is conjugate to a Poisson likelihood function.
If you are interested in seeing more of the material on Bayesian statistics, arranged into a playlist, please visit: • Bayesian statistics: a... Unfortunately, Ox Educ is no more. Don't fret however as a whole load of new youtube videos are coming soon, along with a book that accompanies the series: www.amazon.co.uk/gp/product/1... Alternatively, for more information on this video series and Bayesian inference in general, visit: ben-lambert.com/bayesian-lect... For more information on econometrics and Bayesian statistics, see: ben-lambert.com/

[@xochdt]

This was uploaded 7 years ago, yet it's still saving carees. Thank you so much.

[@toishiki]

Thank you sooo much!! Very clear!

[@isyakumuhammad6982]

thank you very much , this is a very clear video.
could you please provide me a link the explain an inverse Gaussian distribution with power normal prior.
thanks.

[@marlonkakurira9931]

Well understood. Many thanks!!!

[@alessandro64484]

hi, i have a question: is it possible to compute the joint distribution of the poisson? π(lambda,X1,...,Xn)?

[@S4N0I1]

i love you for this

[@danielmburu6936]

wow thanks so much for the info

[@RobertoMartin1]

That was excellent

[@jemimanamale4447]

At 1:23 should the it be x^a-1? I’m seeing different resources using either x or lambda

[@ediltonbrandao8407]

thnks from Brazil

[@lemyul]

thakns for sketching out

[@mashhoodahmad6101]

Dear Sir Please tell how we will deal with if summation (1 to N)(theta -x) as you told summation (1 to N)(X)=N.Xbar

[@choendenkyirong8313]

I think you'd just simplify as such:
sum( theta - x_i ) = sum(theta) - sum(x_i) = n*theta - n*xbar = n(theta - xbar)

[@mashhoodahmad6101]

@@choendenkyirong8313 Thankyou so much Sir