# NavList:

## A Community Devoted to the Preservation and Practice of Celestial Navigation and Other Methods of Traditional Wayfinding

**Re: Still on LOP's**

**From:**Michael Wescott

**Date:**2002 Apr 23, 16:07 -0400

I hesitate to jump in but ... Trevor wrote: > Geoffrey wrote: >> My "proof", as you were kind enough to call it, has nothing to say >> regarding the size of the 'hat. > I did not suggest that it had. But your proof leads to the conclusion > that there is a 0.25 probability that the true position lies within the > cocked hat _regardless_ of the size of that 'hat. That is > counter-intuitive. Either there is a reason why what seems intuitive is > wrong or else there is some error in your proof that I cannot see. I was > wondering whether you could dismiss the latter possibility by pointing > to the reason for disregarding my argument that the number cannot always > be 0.25. Once you have a specific 'hat and a specific true position there is no matter of probability. The true position either lies in the 'hat or it doesn't. It makes no sense to talk of the probability of the true position being in the 'hat; it's either inside or outside. What we can talk about is the pattern or distributions of 'hats that get generated near a true position. As shown before, 25% of the generated 'hats will enclose the true position and 75% will not. There are two assumptions that have to be met for this to be true. First, the distribution of the error must be symmetric around 0, meaning there is no inherent bias in the errors in either direction (toward or away). Second, the errors must be independent in each of the observations. If we add the addition assumption that smaller errors are more likely than larger errors, and we generate a large number of hats then we'll notice several things. First, 25% of the hats will contain the true position. Second, the hats vary quite a bit in size but larger hats enclose the true position more often than smaller ones. And third, there are more smaller hats than larger ones. More smaller hats which tend to be near but not enclosing the true position. Larger ones are rarer but tend to surround the true position. That should help the intuition. But why should this be so? Try this experiment. Put down a dot for a true position. Pick 3 values at random, these are the magnitudes of the error. For each magnitude draw 2 parallel lines at that magnitude distance from the point, one on each side. Use different orientation for each pair. There will now be 8 triangles. Six are smaller and do not enclose the point. Two are larger enclosing not only the point but 3 of the other triangles as well. Each of these 'hats is as likely as any of the others given that set of error magnitudes. Hence, more smaller hats than larger; more larger hats enclose the true position than do smaller hats. >> Similarly, if you only have one 'hat, then placing the MPP in the centre of >> it is the best you can do. > That is indeed the MPP, if the 'hat is equilateral. No. Given 3 LOPs and a 'hat, the MPP is equidistant from each side of the hat; in the center of the inscribed circle. The mutual orientation of the LOPs won't alter that. What is required is that the error distribution be the same for each LOP; that the distribution be symmetric about 0; and that smaller errors be more likely than larger ones. Lots of "ifs" but not unreasonable. A good analogy for finding the MPP is to take rubber bands and tie one end of each together. Attach the other end of each to one of the LOPs in such a way that it is free to move along the length of the LOP. The MPP will be where the knotted end winds up. In the special case of just 3 LOPs, equilibrium will be where the tension on all three rubber bands is the same, each stretched the same amount. The center of the inscribed circle. There remains one question. Is the contour around the MPP elliptical? For that to be true, we have to be more specific about the distribution. If it's Gaussian then the contour is elliptical. Other distributions might also give rise to elliptical contours as well. There are certainly some distributions for which the contour is not ellipse. For example, the triangular shaped distribution mentioned earlier in the thread will give rise to a polygonal contour (at least it will at the 100% confidence level). But I do think we can get a feel for some more general qualities of the contour. Our rubber band analogy is again useful. Intuitively, there will only be one point where the knot comes to rest. I.e there is only one "highest" peak of the contour, and no other smaller "peaks". Less intuitive is the next assertion: that the shape of the contour is convex. But if it weren't we could add more LOPs that cross the concavity (indentation) of the contour and by adding enough of these we'd get a contour with two peaks. A contradiction. This is not a particularly satisfying argument but I suspect only a rigorous mathematical treatment would be. And I've spent too much time on this as is.