bayesian

There's nothing rational at all about the process, other than you're attempting to blindly apply the Bayesian formula.

How would you quickly tell if a Bayesian network aggregating probabilities from hundreds of observations is correct?

If you construct a Bayesian analysis of threats given that meta-model, you'll see that psychology plays a big role.

Using conditional probability, or bayes theorem to calculate a conditional probabilitiy, does not make something bayesian analysis.

This is not a Lisp issue, it's a Bayesian statistics or perhaps a PG issues.

From lurking in the subreddit /r/statistic, Bayesian will tell you why but NN will not.

R has no native bayesian library like Pymc 3 (Must use stan which is c++).

To those that ascribe to Bayesian probability, our first assumption is to assume all valid orientations are part of a uniform distribution.

There are also somewhat better-than-random strategies such as Bayesian optimization and particle swarm optimization that can help you to search more efficiently.

Just because you can use Bayesian nomenclature to snow others, doesn't make it rational.

We are not able to make observations in the Bayesian sense.

I suggest you look at a package that can handle both Bayesian networks and influence diagrams, such as Hugin, Norsys or Genie.

It would be bayesian if they put a distribution on the proportion of people that have a "certain cancer."

Of course, there are also issues with the notion of "observation" used in Bayesian statistics.

The Bayesian framework is an idealization, resting on an ideal notion of "observation".

Currently reading Dune (I'm at 50%): I work a lot with Bayensian models and I keep seeing the way Paul Atreides can "feel" the future as a perfect implementation of Bayesian thinking.

We've had plain old naive-Bayesian filtering for ages - I'm surprised more feed-readers don't use it.

Bayesian analysis in the New York Times.