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Max
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I am trying to analytically compute the posterior distribution for a simple dataset. Y | X = x isI have a multi-variate Gaussian random vector and the likelihood has a non-degenerate density. My prior hasand a discrete uniform distribution such that p(x) = 1/nprior. I am multiplying my prior with my likelihood to get a Gaussian mixture with n terms. I am not sure how to then analytically compute the pdf from this, I don't have any experience with mixture models. Any help would be appreciated!

I am trying to analytically compute the posterior distribution for a simple dataset. Y | X = x is a Gaussian random vector and the likelihood has a non-degenerate density. My prior has a discrete uniform distribution such that p(x) = 1/n. I am multiplying my prior with my likelihood to get a Gaussian mixture with n terms. I am not sure how to then analytically compute the pdf from this, I don't have any experience with mixture models. Any help would be appreciated!

I am trying to analytically compute the posterior distribution for a simple dataset. I have a multi-variate Gaussian likelihood and a discrete uniform prior. I am multiplying my prior with my likelihood to get a Gaussian mixture with n terms. I am not sure how to then analytically compute the pdf from this, I don't have any experience with mixture models. Any help would be appreciated!

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Max
  • 1
  • 1

Compute posterior distribution of Gaussian likelihood and discrete uniform prior

I am trying to analytically compute the posterior distribution for a simple dataset. Y | X = x is a Gaussian random vector and the likelihood has a non-degenerate density. My prior has a discrete uniform distribution such that p(x) = 1/n. I am multiplying my prior with my likelihood to get a Gaussian mixture with n terms. I am not sure how to then analytically compute the pdf from this, I don't have any experience with mixture models. Any help would be appreciated!