weidade37211

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These are questions asked by weidade37211

I know you can call python from Maple, I am thinking if there is the other way around. That is use Maple (and its toolbox) as backend engine to do calculations (e.g. Global Optimization), and say manipulate the data in Python as the front-end.

 

Assume I know some relations like 0<a<b<c<1, n is positive integer, and I want to know if

a^(n+1)-b^(n+1)<c * (a^(n)-b^(n)) holds. How to check this in Maple? Is there any command like verifiy evalb for this kind of problem.? Or solve the inequality in terms of c, given the relations of a,b,c?

Thanks.

Hi all, my maple will crash when calculating this sheet.. Any help here?
 

``

``

restart

``

Digits := 15

15

(1)

NULL

NULL

``

c := .95

.95

(2)

``

theta := .9

.9

(3)

k := 1

1

(4)

p_l := 10^(-15)

1/1000000000000000``

(5)

n := 10^10

10000000000

(6)

``

fsolve(c = p_l^k*(1-p_l)^(n-k)*theta/(p_l^k*(1-p_l)^(n-k)*theta+p^k*(1-p)^n*(1-theta)), p = 10^(-6), 1.09*10^(-10) .. .1)

``

``

``

NULL

``

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``


 

Download test.mw

For a very simple sheet:

restart;

with(Statistics);

X := RandomVariable(Uniform(0, 1))

Sample(X, 2)

Maple always show: 0.8147236863931789, 0.9057919370756192 when I click the "execute the entire sheet." on the top.

While, if I only execute Sample(X, 2), then it seems generate random samples.

Why? is this because of the " Pseudo-random algorithm " built in Maple?

 

 

 

I am using the Jeffery's prior for Binomial models, i.e. a beta prior distribution of beta(0.5, 0.5).

If we see n failure free trails, it is easy to know the posterior distriution is beta(0.5, 0.5+n).

Now I want to solve (plot..actually) the required n for specifced posterior confidence level and a bound.

I am using maple sources code as in the picture. But it takes very long time to plot n, given c=0.95, p=10^(-10)..10^(-7). Any idea to speed up the plots? Thanks!

 

 

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