CENTRAL LIMIT THEOREM (GAUSSIAN) MODEL

 

This model demonstrates Nova’s ability to process statistical data and create a histogram, and its use of plug-ins. The submodel tosser represents a coin being flipped. There
is a term flip that models the result of a coin toss.  Double click the flip term to view the formula. 
flip = (Math.random() < wt) ? 0 : 1,
where wt is input from the top-level model. This means that if the random number generated between 0 and 1 is less than wt, flip=0, and otherwise flip=1. If, for example, wt = .5, then roughly half the time flip=0, and roughly half the time flip=1.

               

The stocks heads and tails accumulate flip totals. If you run 100 flips with a weight of .5, you will get a heads/tails total of around 50/50. Of course, the actual outcome cannot

be predicted. We now wish to repeat the 100-flip experiment 1000 times, and plot a histogram showing the total number of heads.

 

Clicking Load/Exec produces 1000 runs of the 100-flips; at the end of each 100-flip run the total number of heads is recorded in the histogram and displayed in the graph. With

a fair coin, this histogram assumes a Gaussian shape. Tosser is a clocked chip designed to implement the 100-flip experiment. (A clocked chip runs a complete simulation (in

this case a 100-flip coin toss) for each time interval of the main simulation.) 

                                 

A Histogram display component shows the results. The heads_per_100 graph shows the number of heads per 100 flip experiment.

               

THE MODEL (Gaussian, Top Layer)  

THE MODEL (Flip, Bottom Layer)

THE MODEL LAUNCH