In the univariate case, there is no max/min possible value. We just have the variance to say how unlikely a value is that is far from the distribution mean, though any value is possible. Same in multivariate, so I don't think you could say the distribution fits strictly inside a sphere.
The distribution will only be symmetrical and not 'elongated' if the variances are the same, which is the case I think you're talking about. Ted I am also confused by the naming in this class. What I'd imagine is the vector of means is called "offset". The variances come in to the picture via a matrix called "mean". (That's not the covariance matrix right? might expect that from an API perspective but I don't think that's how it is used.) And the parameter for the case where all variances are the same is "radius". On Wed, Nov 14, 2012 at 8:32 AM, Dan Filimon <[email protected]>wrote: > Hi, > > I'm familiar with the basic univariate normal distribution but am > having trouble understanding how the Mahout multivariate normal > distribution works. > > Specifically, what does the radius of the distribution stand for? > What I'm imagining (at lest for 3 dimensions) is that all points would > fit into a sphere centered in the mean with the given radius and that > they would be normally distributed inside. > > This however doesn't seem to be the case (unless my tests are broken). > > What am I missing? > Thanks! >
