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This is an attempt to give a pragmatic overview, without the math. Bayesian methods rely on the concept of "priors". A prior is a known probability about a fac
by wamatt 14y ago
This is an attempt to give a pragmatic overview, without the math.
Bayesian methods rely on the concept of "priors". A prior is a known probability about a fact, or event, or whatever it is you are modeling. Priors "seed" the network.
Bayesian networks generally need fewer samples of data to make predictions, and as a result the downside is the sensitivity of the data (aka priors) increases. Whereas frequentist approaches rely on much larger data sets that can handle noise, more effectively.
Think about GMail's spam filter for example (A Bayesian approach), if you train HAM as SPAM, that is going to have devastating effect on the efficacy of your filtering.
Thus practically speaking, if you have tons of data and looking for a signal, use frequentist. If you're building, for example, an expert diagnostic engine (think Sherlock Holmes), requiring few pieces of information to make a prediction, consider using Bayesian.
I'm over-simplifying of course, but that seems to be the gist of it.
- othermaciej 14y agoI don't think that's right. If you have enough data points, your prior gradually gets more relevant. And Bayesian statistics has the concept of an "ignorance prior", which mathematically represents the position where all possibilities are equally likely. Bayesian statistics also offers the possibility of adding more data after running your experiment, and computing a new answer in a consistent way. Whereas doing this with frequentist statistics completely is completely invalid.
- wamatt 14y ago"Ignorance priors" are not the common use case for Bayesian in general, at least in my experience. Furthermore, who said adding more data is not allowed? Of course it is, and I agree fully, but do struggle to see how that contradicts what was said. Think about a Bayesian spam filter, you keep training it with more "priors", over time and it gets better.