bbchop & Wikipedia's Bayesian search theory page

4 messages, 3 authors, 2016-06-15 · open the first message on its own page

bbchop & Wikipedia's Bayesian search theory page

From: Johannes Schindelin <hidden>
Date: 2016-06-15 22:47:15

Hi,

I tried to find some documentation for Bayesian search theory, but it 
seems those ridiculous Wikipedia admins struck once again, in their 
mission to reduce the world's intellect to their own.

Anybody know where I can find information about Bayesian search theory 
that is not deleted by people envious of other people's brains?

Thanks,
Dscho

P.S.: yes, I am disappointed.  "Wisdom of the crowds"?  Not with this type 
of human beings in control of articles other people wrote.

GitHub linking, was Re: bbchop & Wikipedia's Bayesian search theory page

From: Johannes Schindelin <hidden>
Date: 2016-06-15 22:47:15

Hi,

On Sun, 16 Aug 2009, Johannes Schindelin wrote:
I tried to find some documentation for Bayesian search theory, but it 
seems those ridiculous Wikipedia admins struck once again, in their 
mission to reduce the world's intellect to their own.
Ah, never mind, it seems that they did not delete _this_ page (it would 
have been the third I looked for this week which got deleted and made 
extra hard to find a copy of).

The problem, really, is that the link on the bbchop GitHub site is wrong:

	http://github.com/Ealdwulf/bbchop/tree/master

The issue is that the link incorrectly includes the closing parenthesis.  
It should link to

	http://en.wikipedia.org/wiki/Bayesian_search_theory

not

	http://en.wikipedia.org/wiki/Bayesian_search_theory)

Scott, is it possible to fix that?  Or is the README not magically made 
from the README in the repository (which does not contain HTML markup)?

Ciao,
Dscho

Re: GitHub linking, was Re: bbchop & Wikipedia's Bayesian search theory page

From: Scott Chacon <hidden>
Date: 2016-06-15 22:47:15

Hey,

On Sun, Aug 16, 2009 at 10:18 AM, Johannes
Schindelin[off-list ref] wrote:
Hi,

The issue is that the link incorrectly includes the closing parenthesis.
It should link to

       http://en.wikipedia.org/wiki/Bayesian_search_theory

not

       http://en.wikipedia.org/wiki/Bayesian_search_theory)

Scott, is it possible to fix that?  Or is the README not magically made
from the README in the repository (which does not contain HTML markup)?
If they change the README to have spaces between the url and the
parens, it will link properly - I'll file a bug for the linking issue.
 Thanks.

Scott

Re: bbchop & Wikipedia's Bayesian search theory page

From: Ealdwulf Wuffinga <hidden>
Date: 2016-06-15 22:47:15

On Sun, Aug 16, 2009 at 6:13 PM, Johannes
Schindelin[off-list ref] wrote:
I tried to find some documentation for Bayesian search theory, but it
seems those ridiculous Wikipedia admins struck once again, in their
mission to reduce the world's intellect to their own.
It looks like it is still there to me:
http://en.wikipedia.org/wiki/Bayesian_search_theory

It looks like github has included a ')' on the end when html-ifying
the link inthe README, making it into a dead link. I'll fix that.

The wikipedia article is still not amazing,though. Unfortunately most
of the online descriptions
of Bayesian Search Theory, such as:
http://www.sarinz.com/index.cfm/3,112,261/landsearchmethodsreview.pdf
seem to go heavily into the minutia of search-and-rescue, which while
interesting, is not
relevant to git.

However, although I got the idea of bbchop from search theory, it is
not necessary to know much
of search theory in order to understand bbchop. The basic algorithm is
very simple:

At each step, test the commit for which the expected gain of information (about
the location of the bug) is greatest.

That is basically all I got from search theory so far - the
calculation of the probability of the
bug existing in each location is standard bayesian probability theory,
which maybe you already
know. If not, a very readable reference is:
http://www.inference.phy.cam.ac.uk/mackay/itila/book.html (free on-line book).

So all the code does is compute N entropies and pick the best. Most of the
complexity is introduced by:
 - calculating the N entropies without calculating N^2 probabilities
 - calculations over a DAG.

Ealdwulf
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