QuoteOriginally posted by: PdISince the event{X₁+X₂=y₁,X₁/(X1+X2)=y₂}is equivalent with the event{X₂=y₁(1-y&#8322,X₁=y₂y₁}we obtain for the densityf(y1,y2)=exp(-y1*(1-y2))*exp(-y1*y2) =exp(-y1)yikes.. you transformed the space (plane) without accounting for the jacobian

<t>is there any catch in this one ?l>b; aba <=l <b; 0.5(a(l^2-a^2)^0.5 + l^2 taninv(a/(l^2-a^2)^0.5)) 0=< l <a; pi l^2/4 if the dog is tied outsidel>a+b; 0.25pi(3l^2+(l-a)^2 + (l-b)^2) - {donkeywork}; where donkeywork is the overlapping areab<=l<a+b; 0.25pi(3l^2+(l-a)^2 + (l-b)^2)a<=l<b; 0.25pi(3l^2...

<t>QuoteOriginally posted by: iliketopologyQuoteOriginally posted by: mahalekrishna okay, lets hav a look at this onelet m(t) = cX(t/c^2)X(t) - X(s) ~ N(0, t-s)... this is just definition (mere representation)hence, X(t/c^2) - X(s/c^2) ~ N(0, (t-s)/c^2).. for the moment lets assume c>0P((m(t)-m(s))<...

Hi,Anyone who has used the LogL object in EViews.. please share your file with your model (one that didnt work, the model i.e... the code needs to be working), for reference. I will be forever indebted.

<t>QuoteOriginally posted by: iliketopologyQuoteOriginally posted by: mahalekrishnai think it follows from definition X(t) - X(0) ~ N(0,t).. X(t/c^2) - X(0) ~ N(0,t/c^2), from here we can compute dP(cX(t/c^2) -cX(0) <=z)/dz (even this is not needed.. the specific corollary for normal variates can be...

i think it follows from definition X(t) - X(0) ~ N(0,t).. X(t/c^2) - X(0) ~ N(0,t/c^2), from here we can compute dP(cX(t/c^2) -cX(0) <=z)/dz (even this is not needed.. the specific corollary for normal variates can be used if eps~N(mu, sig^2) then k*eps~N(k*mu, (k*sig)^2) ))

<t>I think marginal and conditional densities mean the same thing, they mean that we have to keep some variables constant and integrate the pdf wrt the other variables over the entire domain, something like P(-inf<x1<inf and -inf<x2<inf|x3=k3 and x4=k4), so we have marginal distribution as some func...

<t>QuoteOriginally posted by: AlanTo answer your other post, the general motivation for the square-root ruleis an iid assumption. Suppose R(i), where R(i) is the simple return in period i is drawn independently each period from an identical distribution.Then, the final wealth after N periods is W(N)...

<t>hi.. i have a way, but dont know whether it holds good (summable) for a hyper-dice (h). my way is fairly simple . Let mi be the probability of occurrence of the i-th side (std ritual: sum(mi)=1) and let the die-roll end in the r-th try with the k-th side. sigma(xi)=r-1 such that xi is N, xi>=1 fo...

i can provide an amateurish view.. since the decision is conditional on knowing the rand no 'e' and the number on first card 'z0', for the argument to be true 'p(z>z0|e>z0)' should be meaningful, but i am unable to find any such meaning

<t>i have a amateurish approach.. sadly i am an amateur actually the prob(y1<z1 and y2<z2)=p(z1&z2) can be calculated within the region bounded by x1, x2>=0, x1+x2<=z1 and x2>=(1/z2-1)x1, for the integrand exp(-x1-x2).This trivially reduces to p(z1&z2)=z2(1-exp(-z1)-z1exp(-z1)) for 0<=z2<=1 ...

I am a bit confused regarding this, most researchers have concentrated on better process specifications and many famous pricing formulae price assets using first moments.