'Distance' between two normal distributions

If I have two normal distributions N(m1, s1) and N(m2, s2), what is a good measure of the distance between them? I was thinking of something like a K-S distance like max|phi1-phi2|. I know it probably depende on what I want it for, or what exactly I mean by distance, but any ideas would be

linear regression with missing data

Say I have three set of data x,y,z. x and z consist of data over the time interval (0,T), but for y, there is missong data, and we only have data on y on (S,T) with 0ST. I want to perform a least square regression of z onto x and y. How do I account for the missing data of y? --

Re: random walk in 2d

PROTECTED] (Francis Dermot Sweeney) wrote: Here is a problem that is quite tricky. Starting at a radius R_o, a hop is made of length from the current point to the origin (R_o), in a random, uniform direction, on a 2d plane. This take us to a new point, with distance to the origin R_1. The next hop

2d random walk

Here is a problem that is quite tricky. Starting at a radius R_o, a hop is made of length from the current point to the origin (R_o), in a random, uniform direction, in 2d. This take us to a new point, with distance to the origin R_1. The next hop is then of length R_1, in a random uniform

random walk in 2d

Here is a problem that is quite tricky. Starting at a radius R_o, a hop is made of length from the current point to the origin (R_o), in a random, uniform direction, on a 2d plane. This take us to a new point, with distance to the origin R_1. The next hop is then of length R_1, in a random