June 2014
Intermediate to advanced
241 pages
6h 20m
English
Content preview from Bayesian Networks
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Theory and Algorithms for Bayesian Networks 105
> gs(cropdata1, test = "cor", alpha = 0.05, debug = TRUE)
[...]
----------------------------------------------------------------
* learning the markov blanket of N .
[...]
* checking node V for inclusion.
> N indep. V given ' C ' (p-value: 0.3048644).
* checking node W for inclusion.
> node W included in the markov blanket (p-value: 7.89e-07).
> markov blanket ( 2 nodes ) now is ' C W '.
> restarting grow loop.
* checking node V for inclusion.
> N indep. V given ' C W ' (p-value: 0.1416876).
* checking node W for exclusion (shrinking phase).
> node W remains in the markov blanket. (p-value: 7.89e-07)
[...]
When learning the Markov blanket of N, we have that node C is included
before V. Apparently, the dependence ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.