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Dear Maple experts,

I would like to generate population data that is the best possible approximation of a multivariate normal distribution with a specified covariance matrix and vector of means. I do not want to draw a sample from a multivariate distribution, but I want the population values itself which are approximately multivariate normal distributed. The size of the datamatrix should be limited, otherwise I could draw a huge sample from a multivariate normal distribution. For instance, I would like to generate a 200 by 6 data matrix that is the best (or at least good enough) approximation of a MVN distribution. For a bivariate normal distribution one could calculate the probalities of a grid by integrating the density, but for six variables that seems undoable. 

Before trying the invent the wheel again, I think I will ask this question to experts, because it is unlikely that there is no already existing algorithm that does the job pretty well.

Thanks in advance,

Harry Garst

Hello, Please how do I compute cdf of student t distribution in maple Tξ+1(.). I have a function that i nvolves student t distribution but finding it difficult to compute student t in the funcion. I am new to maple.

Hi everyone

I'm currently working on some mandate distribution using "Jefforson's Method" but I have run into some problems.

The general form of the calculations I do is as follows:

d:=fsolve(m = floor(v1/x)+floor(v2/x), x)

But in the case of m=5, v1=4969 and v2=208 it does not work. If I change v1 a bit it works as a charm but when 
4960=<v1=<4969 it does not.

Can any of you figure out why?

 

The equation surely has a solution (well, a lot of solutions). I can figure some out just by estimating and trying. Furthermore, wolfram alpha easily gives me several solutions:

http://www.wolframalpha.com/input/?i=5+%3D+floor%284969%2Fx%29%2Bfloor%28208%2Fx%29

So how come I cannot get Maple to solve it?

 

Thanks in advance!

Hello,

would you please help me how can i introduce a probability distribution function to maple in document mode?

I want to calculate integral of x f(x)dx, while I want maple to know f(x) is a probability distribution function.

I do not have any assumption about f(x)(for example normal or exponential distriburion)

Thanks

F := ((1/6)*z+(1/3)*z^2)/(1+(1/6)*z+(1/3)*z^2);

Dist := subs(z=t,F);

RealDist := Distribution(CDF = unapply(Dist(t-1),t));

X:=RandomVariable(RealDist);

CommonDensity := PDF(X,t);

 

F do not have D(t) but density have D(t) , what is D(t)?

I am trying to get Maple to calculate for me.. Let X and Y have a trinomial distribution with parameters n=3, p1=1/6 and p2=1/2. I am supposed to find E(X), E(Y), Var(X), Var(Y) and Cov (X,Y). Thank you to anyone who can assist with this. And if  you can help me understand the concept behind it.. Im having some troubles with bivariate distributions in my Stats course :( Thanks!

Hi all,

I have been trying to plot in Maple a Beta Prime Distribution using the Statistics package. I have define it through its density function and its range with the command

U := Distribution(PDF = (proc (x) options operator, arrow; x^(alpha-1)*(1+x)^(-alpha-beta)/Beta(alpha, beta) end proc), Support = 0 .. infinity)

and then assigned it to a random variable Z with the command

Z := RandomVariable(U)

Now I wanted to plot the density...

Hello,

I am trying to get a shaded area in my plot but I could not.

First we solve ODE without randomness:

ode := diff(U(t), t) = -(A+B*U(t))*U(t);

Then we add randomness to ODE and solve:

ode2 := diff(U(t), t) = -(A+r(t)+B*U(t))*U(t);

A with randomness for r in R=( - 0.0001/365, 0.0001/365) is:

A(t,r)= A+r

Where A is constant =  0.0001/365

We plot both solution. For the plot I...

Custom distribution
restart;
with(Statistics):
Density := 2*exp(-t^2)*sqrt(Pi);
Dist := int(Density, t);

a.

RealDist := Distribution(Dist(t-1));
Error, (in Statistics:-Distribution) unexpected argument(s): Pi(t)*(erf(t))(t)

b.

RealDist := Distribution(PDF = (t->2*exp(-t^2)*sqrt(Pi)), CDF = (t->Dist(t)), Mean = 1);

RealDist := Distribution(PDF = (t->2*exp(-t^2)*sqrt(Pi)), CDF = (t->Dist(t)));
X:=RandomVariable(RealDist(t-1));

To plot the density function of the continuous uniform distribution on [-1,1], my initial attempt was: 

plot(Statistics:-PDF(Statistics:-RandomVariable(Uniform(-1,1)),x), x = -1.1 .. 1.1);

See plot below.


But I wanted something more like the wikipedia image (without the labels, naturally):

See plot below.

In words, I expected a horizontal line on the left of x=-1 and on the right of x=1 (at y=0), and I expected no vertical line at the x=-1 and x=1 points ...

can i distrubute some random particle with an arbitrarity function in maple, for example p(r,theta,phi)=p0*sin(theta)/r, 0.01<r<1, 0< theta<pi, 0<phi< 2*pi. For such distribution we expect many particle in equator.

many thanks

I have been using random numbers in other applications than Maple. Usually there is a function, which will give a pseudo random real number between 0 and 1. When I looked for it in Maple I got quite confused, because there are a lot of different options here - obviously because Maple can deliver random numbers/objects in many ways, even following a certain distrubution. I found out it doesn't work by just using rand(), since it is always starting with the same value. Then I found the command randomize(...

Dear Maple Users

Let the density of electronic states be denoted

I want to create a package, lets call it Accellib. I want to be able to load it using the usual with(Accellib); construct.

So I have created a module, included the option package and right now one procedure, the name of which is exported. Within the worksheet; this works. This package will grow as more stuff gets added. I want to put it into a directory of my choice, which is NOT the directory where Maple stores the packages from its distribution; I like to keep them...

Want create my own geometric distribution with rather only natural values i.e. without zero:

c:=Distribution(PDF=(t->PDF(Geometric(p),t-1)));
But such form is luck to calculate even Mean value:

simplify(Mean(c)) fails. How to apply to get even Mean(F(c)) for complicated enough F?

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