Hey , I am looking for how to calculate the interval of the gamma density distribution when setting the priors in Bayesian estimation. For beta(a,b) the mean of X= E(X)=a/(a+b) and variance is V(X)=(a+b)/(a+b+1)(a+b)^2, as we define the mean and varaince from the common values in the literature I return and calculate a and b. Please for gamma (a,b) distribution with E(X)=0.74 and std(X)=0.0056 how to find a and b? Many thanks in advance.
I have solved it. Today, I have understood, that likelihood has to be computed on likelihood of the bayes condition, even if prior is give. I was not aware of this concept.
5. Today, Sasha checked their weight several times with different scales observing (in kilograms): 92, 82, 83, 86, 86, 90, 83, 84, 89, 85. Assume that the data is normal with variance σ 2 = 9 and a prior distribution for the true weight µ ∼ N(80, 100). (a) What is the posterior distribution? (b) Compute the credible interval of 95% for µ a priori and a posteriori. (c) Compare both intervals with the frequentist 95% confidence interval. Can you conclude that I was optimistic?
Hey , I am looking for how to calculate the interval of the gamma density distribution when setting the priors in Bayesian estimation. For beta(a,b) the mean of X= E(X)=a/(a+b) and variance is V(X)=(a+b)/(a+b+1)(a+b)^2, as we define the mean and varaince from the common values in the literature I return and calculate a and b. Please for gamma (a,b) distribution with E(X)=0.74 and std(X)=0.0056 how to find a and b? Many thanks in advance.
oh! amazing please finish it. thank you vary much!
Thank you so much for this amazing video!!!!!
why is it mu - ybar in the likelihood and not ybar - mu?
Great, please could anyone recomend me additional material (books, demostrations :) ), i need practice too much...
please calculate bayes factor for this
Does the video seem to end abruptly to you? Or was that the end of the derivation?
I wonder too
No, that was the entire derivation.
you saved me with this explanation
I have solved it. Today, I have understood, that likelihood has to be computed on likelihood of the bayes condition, even if prior is give. I was not aware of this concept.
Thank-you so much for the clear explanation.
5. Today, Sasha checked their weight several times with different scales observing (in
kilograms): 92, 82, 83, 86, 86, 90, 83, 84, 89, 85. Assume that the data is normal
with variance σ
2 = 9 and a prior distribution for the true weight µ ∼ N(80, 100).
(a) What is the posterior distribution?
(b) Compute the credible interval of 95% for µ a priori and a posteriori.
(c) Compare both intervals with the frequentist 95% confidence interval. Can you
conclude that I was optimistic?
It is great video. I am trying to solve a variant of this. David Barber's 8th chaper 28th question, where, the format is same, but given as yi.
Hey, it should be n*y_bar^2/sigma^2 in the constant term. I know this is not important haha but just a reminder lol
very good
great!gamma conjugate wif poisson n beta cjugate wif binom.What else??
thank you :)
Thank You so much :D :D :D
my brain hurts