My first question is :   How can I count the number of recent calls to a procedure (not exprofile) ?

for example:

X := proc ()
Sample(RandomVariable(Normal(0, 1)), 1)[1]
end proc;
X();  X() ;

should return 2
 

My second question is:  

I have a procedure as seen below which I plot in a component plot  Do(%Plot0=X(%Slider_mu,%Slider_sigma,%Slider_t));

The Do call has 3 sliders inputs in the form of mu, sigma, tt.

I know very little about the Kalman filter. The thing that I do know is that the Kalman filter has great potential.

However I am not sure that I want to invest time doing reasearch on it because I am not sure I

will be able to come up with an working model in Maple. Does anyone have any experience with Kalman filter in Maple ?

Does anyone want to explain how such a model works ?!

Let say that I want two have two inputs nods that each should give a random variable and one node

that simply adds them together and plots the result. How could that be accomplished ?!

I can calculate the probability of observing 5 heads after a 10 coin toss as:

n := 10:  p :=0.5 :  x := 5 :

ProbabilityFunction(Binomial(n, p), x);

                                0.2460937500


Now my question is how can I generalize this argument over time and with an Normal(0,1) process.

I have done a lot of research and simulation when it comes to trailing stop losses.

I have managed to get very nice results but only through theoretical reasoning

and simulation. However simulation is not as "pure" as manipulating equations.

What I have not managed to do so far is to manipulate the PDF to get an probability

that the trail stop is going to be triggered for a specified normal distribution (mean, volatility)

and trailing distance.