| numerical recipes (was xerr) [message #64355] |
Thu, 18 December 2008 09:32 |
pgrigis
Messages: 436
Registered: September 2007
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Senior Member |
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I bought it because I only had several chapters from
second edition printed from the online PDF's...
There's some new stuff on random numbers,
wavelets and a few other subjects.
It seems that the programs are in c++ now instead of c,
but anyway I never use the programs directly, I only
steal the algorithms if I need them.
So jeremy, maybe you should borrow one from the
library and decide for yourself ;-)
Here goes another tangent remarks:
I have always been a bit worried about the lack of
real documentation or source code for the IDL random
functions...
Since that's so important for the montecarlo stuff
etc., I think we have a right to know what goes
on under the hood! I mean, if a referee complains
about your random number generator, how can
you answer? You'll answer, I am using "something
similar to ran1()" (to quote the docs)?
So, has anybody implemented instead one of the
new (and better) algorithms proposed in the third
edition of NR?
They seem to be pretty good to me.
I think this is one very important point, but then
maybe I am worrying too much ;-)
Ciao,
Paolo
Jeremy Bailin wrote:
> On Dec 17, 5:08�pm, Paolo <pgri...@gmail.com> wrote:
>> This is discussed for example in
>> section 15.3 in edition 3 of the book
>> "numerical recipes".
>>
>> Ciao,
>> Paolo
>>
>> Vince Hradil wrote:
>>> On Dec 17, 2:27 pm, lakshmi <lax...@gmail.com> wrote:
>>>> Hi,
>>
>>>> I've been using mpfitfun to fit measured values of period (y) and
>>>> distances (x) in a linear equation y = a + bx.
>>>> I would like to know if we can include the measured uncertainties in x
>>>> values too?
>>
>>>> Thanks,
>>
>>>> Lakshmi
>>
>>> Well, since it's a linear problem you should probably choose a linear
>>> solution, not mpfitfun. �Also, you need to take into account the
>>> variance and covariance for both x and y, so you need to solve this
>>> with care.
>>
>>> If you google "fitting a straight line when both variables are subject
>>> to error" you'll get a lot of info:http://tinyurl.com/54m8l3
>>
>>
>
> On a complete tangent... how is the third edition compared to the
> second? I've been hemming and hawing about picking it up.
>
> -Jeremy.
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