Question

How to calculate posterior using data distribution? Posterior = (prior x likelihood) / (normalization) Prior =...

How to calculate posterior using data distribution?

Posterior = (prior x likelihood) / (normalization)

Prior = 0.5

Homework Answers

Answer #1

Suppose the independent sample come from the density function   . So the likelihood function can be written as

Here we have prior= 0.5 ,so posterior can be written as

Since

Thus

So the posterior distribution is given by

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
What are prior and posterior probabilities and how are they related? Give some examples where posterior...
What are prior and posterior probabilities and how are they related? Give some examples where posterior probabilities would be useful.
Let X1,...,Xn∼iid Gamma(3,1/θ) and we assume the prior for θ is InvGamma(10,2). (a) Find the posterior...
Let X1,...,Xn∼iid Gamma(3,1/θ) and we assume the prior for θ is InvGamma(10,2). (a) Find the posterior distribution for θ. (b) If n= 10 and   ̄x= 18.2, find the Bayes estimate under squared error loss. (c) The variance of the data distribution is φ= 3θ2. Find the Bayes estimator (under squared error loss) for φ.Let X1,...,Xn∼iid Gamma(3,1/θ) and we assume the prior for θ is InvGamma(10,2). (a) Find the posterior distribution for θ. (b) If n= 10 and   ̄x= 18.2, find...
Data yi, i = 1, . . . , n arise from a Poisson distribution with...
Data yi, i = 1, . . . , n arise from a Poisson distribution with rate parameter λ. If the data are y = 17,25,25,21,13,22,23 find the posterior for λ given the above specified Gamma prior. Comment on the posterior, data, and prior means.
Consider Poisson distribution f(x|θ) = (e^−θ) [(θ^x) / (x!)] for x = 0, 1, 2, ....
Consider Poisson distribution f(x|θ) = (e^−θ) [(θ^x) / (x!)] for x = 0, 1, 2, . . . Let the prior distribution for θ be f(θ) = e^−θ for θ > 0. (a) Show that the posterior distribution is a Gamma distribution. With what parameters? (b) Find the Bayes’ estimator for θ.
what is the log- likelihood function of the generalized gamma distribution f(x; a,k,λ)?
what is the log- likelihood function of the generalized gamma distribution f(x; a,k,λ)?
Data yi, i = 1, . . . , n arise from a Poisson distribution with...
Data yi, i = 1, . . . , n arise from a Poisson distribution with rate parameter λ. (a) Write down the likelihood for the model, up to the constant of proportionality. (b) A Gamma distribution is proposed as the prior. How can we use such a prior to include our belief that λ is 10±1 i.e. mean 10 and standard deviation 1?
Data yi, i = 1, . . . , n arise from a Poisson distribution with...
Data yi, i = 1, . . . , n arise from a Poisson distribution with rate parameter λ. (a) Write down the likelihood for the model, up to the constant of proportionality. (b) A Gamma distribution is proposed as the prior. How can we use such a prior to include our belief that λ is 10±1 i.e. mean 10 and standard deviation 1?
Data yi, i = 1, . . . , n arise from a Poisson distribution with...
Data yi, i = 1, . . . , n arise from a Poisson distribution with rate parameter λ. Show that the posterior distribution for λ|y1,...,yn is also Gamma distributed when a Gamma(α,β) prior is used.
Under Kimura-2 parameter model of nucleotide substitution. Calculate the likelihood of the following data: A G...
Under Kimura-2 parameter model of nucleotide substitution. Calculate the likelihood of the following data: A G T C C A T G A T A C G T C G T G C T for 6 genetic distance value between 0 and 1 and plot the results. Using this plot estimate the genetic distance.
For the wave function ψ(x) defined by ψ(x)= A(a-x),x<a ψ(x)=0, x>=a (a) Sketch ψ(x) and calculate...
For the wave function ψ(x) defined by ψ(x)= A(a-x),x<a ψ(x)=0, x>=a (a) Sketch ψ(x) and calculate the normalization constant A. ( b) Using Zettili’s conventions, find its Fourier transform φ(k) and sketch it. (c) Using the ψ(x) and φ(k), make reasonable estimates for ∆x and ∆k. Using p = ħk, check whether your results are compatible with the uncertainty principle.