TBan-I: Difference between revisions
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'''Definition of <math> n(x) </math>:''' | '''Definition of <math> n(x) </math>:'''Given a realization of the random energies <math>{E_1, E_2, \ldots, E_M}</math>, define | ||
<center><math> n(x) = \#\{ i \mid E_{\min} < E_i < E_{\min}+x \} </math></center> that is, the number of random variables lying above the minimum <math>E_{\min}</math> but less than <math>E_{\min}+x</math>. This is itself a random variable. We are interested in its mean value: <center><math> \overline{n(x)} = \sum_{k=0}^{M-1} k \, \text{Prob}[n(x)=k] </math></center> | |||
<center><math> \overline{n(x)} = \ | |||
''' The Final goal''' | ''' The Final goal''' is to show that for large 'M', when the extremes are described by the Gumbel distribution : | ||
is to show that for large 'M', when the extremes are described by the Gumbel distribution : | |||
<center><math> \overline{n(x)} = e^{x/b_M}-1 </math></center> | <center><math> \overline{n(x)} = e^{x/b_M}-1 </math></center> | ||
The key | '''Step 1: Exact manipulations''' | ||
The starting point is the definition of the key quantity | |||
<center><math> \text{Prob}[n(x) = k] = M \binom{M-1}{k} \int_{-\infty}^\infty dE \; p(E) [P(E+x) - P(E)]^{k} \left(1-P(E+x)\right)^{M - k - 1} </math></center> | <center><math> \text{Prob}[n(x) = k] = M \binom{M-1}{k} \int_{-\infty}^\infty dE \; p(E) [P(E+x) - P(E)]^{k} \left(1-P(E+x)\right)^{M - k - 1} </math></center> | ||
Use the following identity to sum over <math>k</math>: | |||
<center><math> \sum_{k=0}^{M-1} k \binom{M-1}{k} (A-B)^k B^{M-1-k} = (A-B)\frac{d}{dA} \sum_{k=0}^{M-1} \binom{M-1}{k} (A-B)^k B^{M-1-k} = (M-1)(A-B)A^{M-2} </math></center> | <center><math> \sum_{k=0}^{M-1} k \binom{M-1}{k} (A-B)^k B^{M-1-k} = (A-B)\frac{d}{dA} \sum_{k=0}^{M-1} \binom{M-1}{k} (A-B)^k B^{M-1-k} = (M-1)(A-B)A^{M-2} </math></center> | ||
to arrive at the form: | to arrive at the form: | ||
<center><math> \overline{n(x)} = M (M-1) \int_{-\infty}^\infty dE \; p(E) \left[P(E+x) - P(E)\right] (1-P(E))^{M-2}= M \int_{-\infty}^\infty dE \; \left[P(E+x) - P(E)\right] \frac{d Q_{M-1}(E)}{dE} </math></center> | <center><math> \overline{n(x)} = M (M-1) \int_{-\infty}^\infty dE \; p(E) \left[P(E+x) - P(E)\right] (1-P(E))^{M-2}= M \int_{-\infty}^\infty dE \; \left[P(E+x) - P(E)\right] \frac{d Q_{M-1}(E)}{dE} </math></center> | ||
'''Gumbel limit ''' | '''Step 2: the Gumbel limit ''' So far, no approximations have been made. To proceed, we use <math> Q_{M-1}(E)\approx Q_M(E)</math> and its asymptotics Gumbel form: | ||
So far, no approximations have been made. To proceed, we use <math> Q_{M-1}(E)\approx Q_M(E)</math> and its asymptotics: |
Revision as of 13:36, 31 August 2025
Nei seguente esercizio useremo le notazioni della statistica dei valori estremi usate nel corso.
exercise 1: La distribuzione di Gumbel
esercizio 2: The weakest link
Exercise 3: number of states above the minimum
Definition of :Given a realization of the random energies , define
that is, the number of random variables lying above the minimum but less than . This is itself a random variable. We are interested in its mean value:
The Final goal is to show that for large 'M', when the extremes are described by the Gumbel distribution :
Step 1: Exact manipulations The starting point is the definition of the key quantity
Use the following identity to sum over :
to arrive at the form:
Step 2: the Gumbel limit So far, no approximations have been made. To proceed, we use and its asymptotics Gumbel form: