Aug 24, 2015 · The article proves that both definitions are equivalent. So, considering the first definition, your question can be rephrased as "why is matrix normal distribution defined that way?" If you look at …

Dec 15, 2022 · The orientation of the ellipse of the distribution is encoded in the covariance matrix. The covariance matrix encodes the variance as well as the orientation of the elliptical distribution so that …

So if you need multivariate normal samples you've got to generate them using a valid (meaning symmetric positive definite) covariance matrix. But then, when you generated the random vectors, …

Finding the Fisher's Information in a normal distribution with known $\mu$ and unknown $\sigma^ {2}$ Ask Question Asked 7 years, 8 months ago Modified 6 years, 5 months ago

Aug 1, 2024 · PS: What's the benefit to write it in matrix variate normal form, compared to multivariate normal form?

Mar 10, 2019 · It will be the expected value of the Hessian matrix of $\ln f (x;\mu, \sigma^2)$. Specifically for the normal distribution, you can check that it will be a diagonal matrix.

Mar 20, 2015 · If you choose from a multivariate normal with a certain correlation, generally the sample correlation will not equal the population correlation. If the idea is to make the sample correlation …

May 9, 2019 · Fisher information matrix for normal distribution Ask Question Asked 6 years, 7 months ago Modified 6 years, 7 months ago

I need to prove that using maximum likelihood estimation on both parameters of normal distribution indeed maximises likelihood function. So, the log-likelihood function for parameters $\\sigma$ an...

Oct 30, 2016 · 82 I was in a seminar today and the lecturer said that the gaussian distribution is isotropic. What does it mean for a distribution to be isotropic? It seems like he is using this property …