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If the samples have a normal distribution then the added noise simply adds the known s to the variance of the samples. So this affects the likelihood function. Then the posterior can be computed in the usual way. If you are assuming a non-normal distribution things are complicated because you need to determine the distribution of the observation which is now the the the sample and the independent Gaussian noise term.

If the samples have a normal distribution then the added noise simply adds the known s to the variance of the samples. So this affects the likelihood function. Then the posterior can be computed in the usual way. If you are assuming a non-normal distribution things are complicated because you need to determine the distribution of the observation which is now the the sample and the independent Gaussian noise term.

If the samples have a normal distribution then the added noise simply adds the known s to the variance of the samples. So this affects the likelihood function. Then the posterior can be computed in the usual way. If you are assuming a non-normal distribution things are complicated because you need to determine the distribution of the observation which is now the sample and the independent Gaussian noise term.

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source | link

If the samples have a normal distribution then the added noise simply adds the known s to the variance of the samples. So this affects the likelihood function. Then the posterior can be computed in the usual way. If you are assuming a non-normal distribution things are complicated because you need to determine the distribution of the observation which is now the the sample and the independent Gaussian noise term.