TBan-I: Difference between revisions

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Consider the <math>M</math> energies <math>E_1, \dots, E_M</math> as independent and identically distributed (i.i.d.) random variables drawn from a distribution <math>p(E)</math>. It is useful to introduce the cumulative probability of finding an energy smaller than ''E''
Consider the <math>M</math> energies <math>E_1, \dots, E_M</math> as independent and identically distributed (i.i.d.) random variables drawn from a distribution <math>p(E)</math>. It is useful to introduce the cumulative probability of finding an energy smaller than ''E''
<center> <math>P(E) = \int_{-\infty}^E dx \, p(x)</math> </center>
<center> <math>P(E) = \int_{-\infty}^E dx \, p(x)</math> </center>
We define:
<center> <math>E_{\min} = \min(E_1, \dots, E_M)</math> </center>
Our goal is to compute the cumulative distribution:
<center> <math>Q_M(E) \equiv \text{Prob}(E_{\min} > E)</math> </center>
for large <math>M</math>. To achieve this, we rely on three key relations:

Revision as of 16:31, 6 August 2025

Detour: Extreme Value Statistics

Consider the M energies E1,,EM as independent and identically distributed (i.i.d.) random variables drawn from a distribution p(E). It is useful to introduce the cumulative probability of finding an energy smaller than E

P(E)=Edxp(x)

We define:

Emin=min(E1,,EM)

Our goal is to compute the cumulative distribution:

QM(E)Prob(Emin>E)

for large M. To achieve this, we rely on three key relations: