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Jun
17
comment Solving a system of polynomials in $N$ variables
wow great. C_1 = 0 C_2=1 C_3=0 C_4=3 C_5=0 C_6=15 C_7=0 C_8=105
Jun
17
comment Solving a system of polynomials in $N$ variables
I am actually interested in matching the first $l$ moments of a given distribution (the $(C_p)_{p=1,...,l}$ with the moments of an equally-weighted Dirac mixture.
Jun
16
comment Solving a system of polynomials in $N$ variables
@VictorLiu : Almost but not quite. Note I take $(v_i)^p$ and not $|v_i|^p$ in my sums, so they are not norms.
Jun
16
asked Solving a system of polynomials in $N$ variables
May
19
revised Continuity of quantiles as function of measure.
added 22 characters in body
May
19
comment Continuity of quantiles as function of measure.
Yes, $\alpha \mapsto Q_{\alpha}$ is continuous at all continuity points of the CDF of $\mu$ but I am interested in continuity w.r.t. the measure.
May
18
asked Continuity of quantiles as function of measure.
Aug
2
awarded  Popular Question
Jul
2
awarded  Curious
May
5
awarded  Tumbleweed
Mar
12
comment Differentiability of function defined as integral
Nice. Thank you very much. Is there any way to determine the (non-)differentiability of $F$ without actually computing $$\int_0^1 \frac{f(t,h) - f(t,0)}{h} dt$$ ? My question is coming from a real problem where I can compute derivatives of $f$ w.r.t $x$ but cannot explicitly integrate $f$ w.r.t $t$.
Mar
12
accepted Differentiability of function defined as integral
Mar
11
asked Differentiability of function defined as integral
Sep
12
asked Ito vs Stratonovich SDE with irregular time-dependence in coefficients
Jul
2
comment Prove the density of this SDE is not smooth in a parameter
thank you for your answer. I don't quite see how it helps with determining smoothness in the variable $x$, though.
Jun
28
awarded  Promoter
Jun
26
asked Prove the density of this SDE is not smooth in a parameter
Apr
9
answered Martingale inequality
Apr
7
comment Martingale inequality
Well, the inequality holds for all fixed $r$. And, assuming $f$ is continuous in the first variable, I think that for a given $t$, $Y^r_t$ and $Y^t_t$ should be close when $r$ is close to $t$. However, I am not sure if the same is true of $\sup_{t \in [0,T]} Y^r_t$ and $\sup_{t \in [0,T]} Y^t_t$ or the quadratic variations.
Apr
7
comment Martingale inequality
@Did I have found a reference: see proposition 4.24 here statslab.cam.ac.uk/~beresty/teach/StoCal/sc3.pdf