Data science and predictability of outcomes.

[SueEllen]

I read tealeaves. Shout if you want yours doing.

I find coffee grounds more predictable...

[jamesbrown]

Where did I say probabilistic prediction requires historical data? I said it requires a similar event. You could create such an event with which to make your prediction. Stochastic processes don't care for time.

Deterministic prediction never makes use historical data either. That's a major slip up in education that is. They measure the likelihood of events through measurement of some quantity.

Did you do a postgrad stats course or something? All sorts of deterministic models make use of historical data to estimate their parameters without introducing any stochastic terms. As I said, it's a modelling choice. No idea what you're on about with regard to "similar event" or the time invariance of a stochastic process or "likelihood" w/r to a deterministic prediction. I think you're getting confused by stationarity or, perhaps, just in general. An event is simply a subset of a sample space. You don't need a "similar" event to make a prediction, you need a model, which may or may not have parameters to estimate from data.

[GB9]

Having recently worked with a genuine in-memory analytics tool, it does anything except speed things up!

[greenlake]

Why have our resident data scientists not predicted brexit?

I read tealeaves.

[sasguru]

Why have our resident data scientists not predicted brexit?

What a dumb question.
I think alcohol has addled your brain.

[scooterscot]

tea leaves got it wrong Scotland is still there

[sasguru]

Did you do a postgrad stats course or something? All sorts of deterministic models make use of historical data to estimate their parameters without introducing any stochastic terms. As I said, it's a modelling choice. No idea what you're on about with regard to "similar event" or the time invariance of a stochastic process or "likelihood" w/r to a deterministic prediction. I think you're getting confused by stationarity or, perhaps, just in general. An event is simply a subset of a sample space. You don't need a "similar" event to make a prediction, you need a model, which may or may not have parameters to estimate from data.

Scooter is simply a thick fraud. Hth.

[vetran]

Scooter is simply a thick fraud. Hth.

oh dear the 'No brainers' are falling out. Next Darmy will be crying into his milk.

[vetran]

tea leaves got it wrong Scotland is still moaning

FTFY.

[NotAllThere]

Because it's hard to predict crazy black-swan events?

Since a crazy black-swan event is one that's hard to predict, that's fairly self evident. But thanks for sharing.

Why have our resident data scientists not predicted brexit?

Any number of reasons. Perhaps they didn't apply their skills to it. Perhaps they didn't have access to the data. Perhaps the process is chaotic - so deterministic but not predictable (sensitive to very small changes in the initial conditions).