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Nov
17
comment Finite state Markov chain
A finite state (discrete-time) Markov chain is a sequence of random variables $X_1,X_2,\ldots$ all of which take values from a finite set $\mathcal{X}$.
Nov
7
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Oct
23
awarded  Popular Question
Oct
15
comment Maximum likelihood estimate of $N$ (trials) in Binomial
For fixed $x$ ($x=4$), we can write $p(x=4 | \pi,N) = \mathcal{L}(N,\pi)$. So $\pi^*$ is a maximiser for a fixed $N$, I still can not see that why this is true. Shouldn't we show that $N$ is unique in some way? May be if I plot $\mathcal{L}$ wrt $(N,\pi)$, that would shed some light.
Oct
15
revised Maximum likelihood estimate of $N$ (trials) in Binomial
edited body
Oct
14
asked Maximum likelihood estimate of $N$ (trials) in Binomial
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17
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2
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10
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Jun
9
comment Convergence in probability of iid normal random variables
OK since I respect you a lot, I want to check :) Thanks a lot. I calculated again and I think that it is probably correct.
Jun
9
comment Convergence in probability of iid normal random variables
$\epsilon = \frac{\sqrt{2}}{2}\delta$ in my calculation.
Jun
9
comment Convergence in probability of iid normal random variables
Hi Davide. I was just trying to check that $\mathbb P\{|X_n-X_1|>2\delta\}=\mathbb P\{|N|>\delta\}$. It turns out, for the right part of the equality, I find an $\epsilon > 0$ which concludes the same proof as above but $\epsilon \neq \delta$ in my case. In other words, I find $P\{|N|>\epsilon\}$ but $\epsilon \neq \delta$. There is another multiplicative factor. Am I wrong? Just checking. thanks!
Jun
7
accepted Convergence in probability of iid normal random variables
Jun
7
revised Convergence in probability of iid normal random variables
added 4 characters in body
Jun
7
comment Convergence in probability of iid normal random variables
Exactly, thanks, they should be iid.
Jun
6
asked Convergence in probability of iid normal random variables
May
11
comment Fourier transform and domains of functions
Can you give a proof that Fourier transform of $f$ or $g$ is divergent?