On models

(Not of the Vogue persuassion...)

From Wikipedia:

Deterministic vs. probabilistic (stochastic): A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables. Therefore, deterministic models perform the same way for a given set of initial conditions. Conversely, in a stochastic model, randomness is present, and variable states are not described by unique values, but rather by probability distributions.

So for a coin toss, the probabilistic modeler would flip a coin 10 times, record the outcomes, and predict the outcome of the next coin toss based on that probability calculation. The deterministic modeler, on the other hand, would create a model based on data from the physical environment to predict a coin toss. The model may consider air pressure, air movement, the force of the toss, the weight of the coin, the color of the coin tosser's shirt, etc... any available data that may influence the outcome of a coin toss.

In related news, I'm now receiving unsolicited stats jokes. Here's the latest:

By the time Ted arrived at the football game, the first quarter was almost over. "Why are you so late?" his friend asked.

"I had to toss a coin to decide between going to church and coming to the game."

"How long could that have taken you?"

"Well, I had to toss it 14 times."

Now, what are the chances...