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In the wild I think you'd be more likely to use something like PyMC or Stan, which work like Prolly but support arbitrarily complex models. Still, love the ide
by stdbrouw 12y ago
In the wild I think you'd be more likely to use something like PyMC or Stan, which work like Prolly but support arbitrarily complex models.
Still, love the idea.
To learn more about probability theory and Bayes, "Probability Demystified" is pretty good. (The Demystified series is McGraw-Hill's take on For Dummies.) To learn more about probabilistic programming, try http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ http://camdavidsonpilon.github.io/Probabilistic-Programming-.... Or Google for MCMC.
I dunno how you'd use it in everyday programming, but it works really well for any sort of predictions. One of the examples in the "Probabilstic Programming and Bayesian Methods for Hackers" book is ranking a bunch of Reddit posts based on ups/downs but taking into account that fewer total votes equals more uncertainty.
- iamwil 12y agoYeah, this is a new library. I don't expect people to use it in the wild just yet. As for how to use it in everyday programming, I think machine learning techniques and algorithms could probably be made easier. But besides that, I was envisioning just as you can use regular expressions to do a "fuzzy match" on a string, I had wanted something that could do a "fuzzy match" on hashes, so I could branch based on a classification, rather than an exact match using "==". Something like a spam filter would be a common application. Your example about reddit would be another. Basically, making decision not just with the information on hand, but also based on prior examples.