ComputerUser

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i think this because chapter 3 page 86

3.2.1 said  z-transform of this network can be written as

rational function (3.1)

can be downloaded here

http://ishare.iask.sina.com.cn/f/33653381.html

i think this because chapter 3 page 86

3.2.1 said  z-transform of this network can be written as

rational function (3.1)

can be downloaded here

http://ishare.iask.sina.com.cn/f/33653381.html

the general case result (2) still has ztrans, it should be a simple rational function

the general case result (2) still has ztrans, it should be a simple rational function

if it is impossible, how Author derive this? need transformation?

i see that they are quite similar, like just need a few more steps, then success

can it be manually done by observation using below script

restart;
n := 1:
rodrigues := exp(x)*(x^alpha)/n!*diff(exp(-x)*(x^(n+alpha)), x$n):
rodrigues := subs(x=2*p, rodrigues):
rodrigues := subs(alpha=1/2, rodrigues):
rodrigues1 := factor(simplify(rodrigues));
n := 2:
rodrigues := exp(x)*(x^alpha)/n!*diff(exp(-x)*(x^(n+alpha)), x$n):
rodrigues := subs(x=2*p, rodrigues):
rodrigues := subs(alpha=1/2, rodrigues):
rodrigues2 := factor(simplify(rodrigues));
n := 3:
rodrigues := exp(x)*(x^alpha)/n!*diff(exp(-x)*(x^(n+alpha)), x$n):
rodrigues := subs(x=2*p, rodrigues):
rodrigues := subs(alpha=1/2, rodrigues):
rodrigues3 := factor(simplify(rodrigues));

                          -(4 p - 3) p
                    1   /    2            \
                    - p \16 p  - 40 p + 15/
                    4                      
               1    /    3        2              \
             - -- p \64 p  - 336 p  + 420 p - 105/
               24                                 

if it is impossible, how Author derive this? need transformation?

i see that they are quite similar, like just need a few more steps, then success

can it be manually done by observation using below script

restart;
n := 1:
rodrigues := exp(x)*(x^alpha)/n!*diff(exp(-x)*(x^(n+alpha)), x$n):
rodrigues := subs(x=2*p, rodrigues):
rodrigues := subs(alpha=1/2, rodrigues):
rodrigues1 := factor(simplify(rodrigues));
n := 2:
rodrigues := exp(x)*(x^alpha)/n!*diff(exp(-x)*(x^(n+alpha)), x$n):
rodrigues := subs(x=2*p, rodrigues):
rodrigues := subs(alpha=1/2, rodrigues):
rodrigues2 := factor(simplify(rodrigues));
n := 3:
rodrigues := exp(x)*(x^alpha)/n!*diff(exp(-x)*(x^(n+alpha)), x$n):
rodrigues := subs(x=2*p, rodrigues):
rodrigues := subs(alpha=1/2, rodrigues):
rodrigues3 := factor(simplify(rodrigues));

                          -(4 p - 3) p
                    1   /    2            \
                    - p \16 p  - 40 p + 15/
                    4                      
               1    /    3        2              \
             - -- p \64 p  - 336 p  + 420 p - 105/
               24                                 

After adding assuming X > 1, it can not evaluate too
, how to define a range for it to transform successfully?

Bernoulli
charc := 1/(I*X/(-1+exp(I*X)))^n;
Density := int(exp(-I*X*u)/(I*X/(-1+exp(I*X)))^n, X = -infinity .. infinity) assuming X > 1;

Density := int(exp(-I*X*u)/(I*X/(-1+exp(I*X)))^n, X = 1.1 .. infinity)


can not evaluate

WoW!!!!  it success to evaluate

why not set pdf at the same time?

WoW!!!!  it success to evaluate

why not set pdf at the same time?

after using Unapply

RealDist := Distribution(Dist(t-1));
Error, (in Statistics:-Distribution) invalid input: IsKnownDistribution expects its 1st argument, dn, to be of type name, but received Unapply(Pi*erf(t), t)

after using unapply

unexpected argument

after using Unapply

RealDist := Distribution(Dist(t-1));
Error, (in Statistics:-Distribution) invalid input: IsKnownDistribution expects its 1st argument, dn, to be of type name, but received Unapply(Pi*erf(t), t)

after using unapply

unexpected argument

sorry for misunderstanding, just consider all i in small letter or all in big letter

sorry for misunderstanding, just consider all i in small letter or all in big letter

yes, by madan's result, in other words to see how integration of two things result in new distribution

i just want to produce madan's answer not only using gamma, i want to use all different kinds of distributions to do madan's result.

in the link of handbook of distribution's derivation of gamma, told the method

to find gamma that means using other process or your own generating function, you can also using the method and

then integrate with normal distribution, to find madan's result.

i want to investigate how these processes change the tail of normal distribution.

of course, furthermore not only normal distribution.

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