markov

It sounds like a good solution might be a Markov Chain Monte Carlo method.

Yeah, actually it makes a markov chain of words from your past tweets.

You'll build hidden Markov Models to detect patterns in the equities markets.

The content that they're talking about (articles on ehow.com and friends) are generated by Mechanical Turk or some similar arrangement, not by Markov chainers!

They have seemingly lucid moments, but so do Markov chain generators.

What about Markov chain reconstructions of their own comments?

When I played with Markov chains, I used Lovecraft and some scientific papers.

We have another component of the same outlier detection system that does this type of fitting, identifying low probability events using a Bayesian model + Markov Chain Monte Carlo.

Machine learning could refer to linear regression, SVM, hidden markov models, dimensionality reduction, neural nets, or any number of other loosely related methods.

By comparison I've done some markov model experiments based on multi-million word corpora and that gets a lot farther into the territory of regularly producing satisfyingly weird disjunctures.

I never thought that inference on a changing process might require different techniques from the techniques for stationary processes until I had to study Hidden Markov Models for an exam.

I have played around with markov chains trained on a first + last name that could generate made up names but that is as far as I got with it.

I was under the impression that people started to "game" this system (SEO) so much that the Markov-chain approach essentially got useless.

Markov chains need lots of training data to be useful.

This new plan should wash out low quality work as it's no longer profitable to publish pamphlets with great titles and near Markov-chain levels of nonsense.

Your posts frequently seem to have Markov-chain like fragments.