Skip to main content
MathOverflow

Return to Question

fixed title
Link
Jeremy Rickard
Jeremy Rickard
  • 35.2k
  • 2
  • 110
  • 151

Learing Learning a Gaussian from noisy observations

added 14 characters in body
Source Link
ejlouw
ejlouw
  • 121
  • 2

Is it possible to learn a distribution over the parameters ($K=\Sigma^{-1}$ and $\mu$) of a Gaussian from noisy measurements of $X$? (Starting with some appropriate prior over the parameters)

I know the case where the measurements are perfect is established theory and can be found in many text books, but I cannot find a solution for this variation of the problem anywhere.

Is it possible to learn a distribution over the parameters ($K=\Sigma^{-1}$ and $\mu$) from noisy measurements of $X$? (Starting with some appropriate prior over the parameters)

I know the case where the measurements are perfect is established theory and can be found in many text books, but I cannot find a solution for this variation of the problem anywhere.

Is it possible to learn a distribution over the parameters ($K=\Sigma^{-1}$ and $\mu$) of a Gaussian from noisy measurements of $X$? (Starting with some appropriate prior over the parameters)

I know the case where the measurements are perfect is established theory and can be found in many text books, but I cannot find a solution for this variation of the problem anywhere.

Source Link
ejlouw
ejlouw
  • 121
  • 2

Learing a Gaussian from noisy observations

Is it possible to learn a distribution over the parameters ($K=\Sigma^{-1}$ and $\mu$) from noisy measurements of $X$? (Starting with some appropriate prior over the parameters)

I know the case where the measurements are perfect is established theory and can be found in many text books, but I cannot find a solution for this variation of the problem anywhere.