Anyone who uses Google has some notion of pagerank: it is the algorithm
that for any specific request, ranks the most popular sites. The first assignment
of the uncertainty lecture aims to code for a pagerank.
It may be easy enough to follow users from site to site, but describing such
a system gets bogged down in sollipsistic cycles. There are some sites that only
lead to a few others,- like apple pie recipes - so that randomizing algorithm risks running
in circles. To counter this, a dampening factore of 0.85 probability places the program
in popular sites, and 1 - 0.85 probability covers access to any page, including
the one we are on.;it is always possible to get out.
One writes the transitions code, a sampling approach (10000) and an iteration.
There are three corpus sets to test the code against.
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