> What do you mean?
the example from the post of weather events is a simplified version of what i mean
the collected data expresses that there is a probability of 50/50 rain versus shine
the first string of possible outputs is scattered and more random than the real data, so weights are introduced to nuance to the output and the second string is more closely representative of the data
but this introduces a relative psuedo causation, what the author refers to as stickyness
it's reminiscent of the monty hall problem(o)
altering the state of the model causes its probability to change relative to a specific potential, but only within the limited scope of the probable space
choosing one door causes the probabilities associated with the state to change but the system itself is unchanged by the choice
and what you see with the weather is a simulation of the collected data stead the underlying system, which i feel conflates correlation with causation
i feel like i hear less and less about markov chains are they still in research and use?
Markov chains will always be important, because they are a fairly fundamental concept (going back to the early 20th century).
They idea is certainly still widely used: two major classes of applications are Hidden Markov Models (where you attempt to infer the hidden state of a system from observations that depend on the state) [1], and Markov-Chain Monte Carlo (MCMC) [2] (where you sample from a probability distribution function by constructing a markov chain whose equilibrium distibution is that distribution).
is page rank still the most prominent search algo?
PageRank is really a ranking algorithm, rather than a search algorithm. It's been applied to many things other than web searching, and has many variants (e.g. see the review article "PageRank Beyond the Web" [3). That said, Google rankings depend on many things other than PageRank.
i always felt these probabilistic models were a conflation of correlation and causation
What do you mean?
[1]: https://en.wikipedia.org/wiki/Hidden_Markov_model#Applicatio...
[2]: https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
[3]: http://epubs.siam.org/doi/10.1137/140976649