grandes-ecoles

Papers (191)
2025
centrale-maths1__official 40 centrale-maths2__official 42 mines-ponts-maths1__mp 20 mines-ponts-maths1__pc 21 mines-ponts-maths1__psi 21 mines-ponts-maths2__mp 28 mines-ponts-maths2__pc 24 mines-ponts-maths2__psi 26 polytechnique-maths-a__mp 27 polytechnique-maths__fui 16 polytechnique-maths__pc 27 x-ens-maths-a__mp 18 x-ens-maths-c__mp 9 x-ens-maths-d__mp 38 x-ens-maths__pc 27 x-ens-maths__psi 38
2024
centrale-maths1__official 28 centrale-maths2__official 29 geipi-polytech__maths 9 mines-ponts-maths1__mp 25 mines-ponts-maths1__pc 20 mines-ponts-maths1__psi 19 mines-ponts-maths2__mp 23 mines-ponts-maths2__pc 21 mines-ponts-maths2__psi 21 polytechnique-maths-a__mp 44 polytechnique-maths-b__mp 37 x-ens-maths-a__mp 43 x-ens-maths-b__mp 35 x-ens-maths-c__mp 22 x-ens-maths-d__mp 45 x-ens-maths__pc 24 x-ens-maths__psi 26
2023
centrale-maths1__official 44 centrale-maths2__official 33 e3a-polytech-maths__mp 4 mines-ponts-maths1__mp 15 mines-ponts-maths1__pc 23 mines-ponts-maths1__psi 23 mines-ponts-maths2__mp 22 mines-ponts-maths2__pc 18 mines-ponts-maths2__psi 22 polytechnique-maths__fui 23 x-ens-maths-a__mp 25 x-ens-maths-b__mp 24 x-ens-maths-c__mp 20 x-ens-maths-d__mp 20 x-ens-maths__pc 18 x-ens-maths__psi 15
2022
centrale-maths1__mp 48 centrale-maths1__official 48 centrale-maths1__pc 37 centrale-maths1__psi 43 centrale-maths2__mp 32 centrale-maths2__official 32 centrale-maths2__pc 39 centrale-maths2__psi 45 mines-ponts-maths1__mp 25 mines-ponts-maths1__pc 24 mines-ponts-maths1__psi 24 mines-ponts-maths2__mp 24 mines-ponts-maths2__pc 19 mines-ponts-maths2__psi 20 x-ens-maths-a__mp 13 x-ens-maths-b__mp 40 x-ens-maths-c__mp 27 x-ens-maths-d__mp 46 x-ens-maths1__mp 13 x-ens-maths2__mp 40 x-ens-maths__pc 15 x-ens-maths__pc_cpge 15 x-ens-maths__psi 22 x-ens-maths__psi_cpge 23
2021
centrale-maths1__mp 40 centrale-maths1__official 40 centrale-maths1__pc 36 centrale-maths1__psi 29 centrale-maths2__mp 30 centrale-maths2__official 29 centrale-maths2__pc 38 centrale-maths2__psi 37 x-ens-maths2__mp 39 x-ens-maths__pc 44
2020
centrale-maths1__mp 42 centrale-maths1__official 42 centrale-maths1__pc 36 centrale-maths1__psi 40 centrale-maths2__mp 38 centrale-maths2__official 38 centrale-maths2__pc 40 centrale-maths2__psi 39 mines-ponts-maths1__mp_cpge 24 mines-ponts-maths2__mp_cpge 21 x-ens-maths-a__mp_cpge 18 x-ens-maths-b__mp_cpge 20 x-ens-maths-d__mp 14 x-ens-maths1__mp 18 x-ens-maths2__mp 20 x-ens-maths__pc 18
2019
centrale-maths1__mp 37 centrale-maths1__official 37 centrale-maths1__pc 40 centrale-maths1__psi 39 centrale-maths2__mp 37 centrale-maths2__official 37 centrale-maths2__pc 39 centrale-maths2__psi 49 x-ens-maths1__mp 24 x-ens-maths__pc 18 x-ens-maths__psi 26
2018
centrale-maths1__mp 47 centrale-maths1__official 47 centrale-maths1__pc 41 centrale-maths1__psi 44 centrale-maths2__mp 44 centrale-maths2__official 44 centrale-maths2__pc 35 centrale-maths2__psi 38 x-ens-maths1__mp 19 x-ens-maths2__mp 17 x-ens-maths__pc 22 x-ens-maths__psi 24
2017
centrale-maths1__mp 45 centrale-maths1__official 45 centrale-maths1__pc 22 centrale-maths1__psi 17 centrale-maths2__mp 30 centrale-maths2__official 30 centrale-maths2__pc 28 centrale-maths2__psi 44 x-ens-maths1__mp 26 x-ens-maths2__mp 16 x-ens-maths__pc 18 x-ens-maths__psi 26
2016
centrale-maths1__mp 42 centrale-maths1__pc 31 centrale-maths1__psi 33 centrale-maths2__mp 25 centrale-maths2__pc 47 centrale-maths2__psi 27 x-ens-maths1__mp 18 x-ens-maths2__mp 46 x-ens-maths__pc 15 x-ens-maths__psi 20
2015
centrale-maths1__mp 42 centrale-maths1__pc 18 centrale-maths1__psi 42 centrale-maths2__mp 44 centrale-maths2__pc 18 centrale-maths2__psi 33 x-ens-maths1__mp 16 x-ens-maths2__mp 31 x-ens-maths__pc 30 x-ens-maths__psi 22
2014
centrale-maths1__mp 28 centrale-maths1__pc 26 centrale-maths1__psi 27 centrale-maths2__mp 24 centrale-maths2__pc 26 centrale-maths2__psi 27 x-ens-maths1__mp 9 x-ens-maths2__mp 16 x-ens-maths__pc 4 x-ens-maths__psi 24
2013
centrale-maths1__mp 22 centrale-maths1__pc 45 centrale-maths1__psi 29 centrale-maths2__mp 31 centrale-maths2__pc 52 centrale-maths2__psi 32 x-ens-maths1__mp 24 x-ens-maths2__mp 35 x-ens-maths__pc 22 x-ens-maths__psi 9
2012
centrale-maths1__mp 36 centrale-maths1__pc 28 centrale-maths1__psi 33 centrale-maths2__mp 27 centrale-maths2__psi 18
2011
centrale-maths1__mp 27 centrale-maths1__pc 17 centrale-maths1__psi 24 centrale-maths2__mp 29 centrale-maths2__pc 17 centrale-maths2__psi 10
2010
centrale-maths1__mp 19 centrale-maths1__pc 30 centrale-maths1__psi 13 centrale-maths2__mp 32 centrale-maths2__pc 37 centrale-maths2__psi 27
2022 centrale-maths1__psi

43 maths questions

Q1 Matrices Matrix Algebra and Product Properties View
Prove that the application $$\begin{array} { | c l l } \mathcal { M } _ { n } ( \mathbb { R } ) & \rightarrow & \mathbb { R } \\ M & \mapsto & \operatorname { tr } ( M ) \end{array}$$ is a linear form and that $$\forall ( A , B ) \in \left( \mathcal { M } _ { n } ( \mathbb { R } ) \right) ^ { 2 } , \quad \operatorname { tr } ( A B ) = \operatorname { tr } ( B A ) .$$
Q2 Matrices Matrix Algebra and Product Properties View
Show that the application $$\begin{array} { | c c c } \left( \mathcal { M } _ { n } ( \mathbb { R } ) \right) ^ { 2 } & \rightarrow & \mathbb { R } \\ ( A , B ) & \mapsto & \operatorname { tr } \left( A ^ { \top } B \right) \end{array}$$ is an inner product on $\mathcal { M } _ { n } ( \mathbb { R } )$.
Q3 Matrices Matrix Algebra and Product Properties View
Deduce that if $A$ is a matrix in $\mathcal { M } _ { n } ( \mathbb { R } )$ satisfying $A ^ { \top } A = 0$ then $A = 0$.
Q4 Invariant lines and eigenvalues and vectors Compute eigenvalues of a given matrix View
Show that, if $A \in \mathcal { M } _ { n } ( \mathbb { R } )$ is nilpotent, then 0 is an eigenvalue of $A$ and that it is the only complex eigenvalue of $A$.
Q5 Matrices Determinant and Rank Computation View
Determine the trace and the determinant of a nilpotent matrix in $\mathcal { M } _ { n } ( \mathbb { R } )$.
Q6 Proof Proof That a Map Has a Specific Property View
Show that, if $M \in \mathcal { M } _ { n } ( \mathbb { R } )$ is nilpotent, then $M ^ { 2 }$ is nilpotent.
Q7 Matrices Matrix Algebra and Product Properties View
Suppose that $M$ and $N$ are two nilpotent matrices that commute. Show that $M N$ and $M + N$ are nilpotent.
Q8 Matrices Matrix Algebra and Product Properties View
Suppose that $M , N$ and $M + N$ are nilpotent. By computing $( M + N ) ^ { 2 } - M ^ { 2 } - N ^ { 2 }$, show that $\operatorname { tr } ( M N ) = 0$.
Q9 Matrices Eigenvalue and Characteristic Polynomial Analysis View
Prove that a matrix $M$ in $\mathcal { M } _ { 2 } ( \mathbb { R } )$ is nilpotent if and only if $\operatorname { det } ( M ) = \operatorname { tr } ( M ) = 0$.
Q10 Matrices Structured Matrix Characterization View
Show that the only real nilpotent and symmetric matrix is the zero matrix.
Q11 Matrices Linear Transformation and Endomorphism Properties View
Let $A$ be a real antisymmetric and nilpotent matrix. Show that $A ^ { \top } A = 0 _ { n }$, then that $A = 0 _ { n }$.
Q12 Matrices True/False or Multiple-Select Conceptual Reasoning View
Suppose $n \geqslant 3$. Give an example of a matrix in $\mathcal { M } _ { n } ( \mathbb { R } )$ with zero trace and zero determinant, but not nilpotent.
Q13 Matrices Linear Transformation and Endomorphism Properties View
Let $\left( E _ { 1 } , \ldots , E _ { n } \right)$ be the canonical basis of $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$. We denote $V = \sum _ { k = 1 } ^ { n } E _ { k }$.
For $i \in \llbracket 1 , n \rrbracket$, express $E _ { i }$ in terms of $V$ and of $V - 2 E _ { i }$. Deduce that $\mathcal { M } _ { n , 1 } ( \mathbb { R } ) = \operatorname { Vect } \left( \mathcal { V } _ { n , 1 } \right)$.
Q14 Proof Existence Proof View
Let $C _ { 1 } , \ldots , C _ { n }$ be $n$ column matrices in $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$, with $C _ { 1 }$ non-zero.
Prove that, if the family $\left( C _ { 1 } , \ldots , C _ { n } \right)$ is linearly dependent, then there exists a unique $j \in \llbracket 1 , n - 1 \rrbracket$ such that $$\left\{ \begin{array} { l } \left( C _ { 1 } , \ldots , C _ { j } \right) \text { is linearly independent } \\ C _ { j + 1 } \in \operatorname { Vect } \left( C _ { 1 } , \ldots , C _ { j } \right) \end{array} \right.$$
Q15 Proof Existence Proof View
Let $d \in \llbracket 1 , n \rrbracket , \left( U _ { 1 } , \ldots , U _ { d } \right)$ be a linearly independent family in $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$ and $H = \operatorname { Vect } \left( U _ { 1 } , \ldots , U _ { d } \right)$.
Prove that there exist integers $i _ { 1 } , \ldots , i _ { d }$ satisfying $1 \leqslant i _ { 1 } < \cdots < i _ { d } \leqslant n$ such that the application $$\left\lvert \, \begin{array} { c c c } H & \rightarrow & \mathcal { M } _ { d , 1 } ( \mathbb { R } ) \\ \left( \begin{array} { c } x _ { 1 } \\ \vdots \\ x _ { n } \end{array} \right) & \mapsto & \left( \begin{array} { c } x _ { i _ { 1 } } \\ \vdots \\ x _ { i _ { d } } \end{array} \right) \end{array} \right.$$ is bijective.
One may consider the rank of the matrix in $\mathcal { M } _ { n , d } ( \mathbb { R } )$ whose columns are $U _ { 1 } , \ldots , U _ { d }$.
Q16 Proof Bounding or Estimation Proof View
Let $\mathcal { W }$ be a vector subspace of $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$ of dimension $d$. Prove that $$\operatorname { card } \left( \mathcal { W } \cap \mathcal { V } _ { n , 1 } \right) \leqslant 2 ^ { d }$$
Q17 Discrete Probability Distributions Proof of Distributional Properties or Symmetry View
If $X$ follows the distribution $\mathcal { R }$ (where $X ( \Omega ) = \{ - 1,1 \}$, $\mathbb { P } ( X = - 1 ) = \mathbb { P } ( X = 1 ) = \frac { 1 } { 2 }$), specify the distribution of the random variable $\frac { 1 } { 2 } ( X + 1 )$.
Q18 Discrete Probability Distributions Probability Distribution Table Completion and Expectation Calculation View
Calculate the expectation and the variance of a variable following the distribution $\mathcal { R }$, where $\mathcal{R}$ is defined by $X ( \Omega ) = \{ - 1,1 \}$, $\mathbb { P } ( X = - 1 ) = \mathbb { P } ( X = 1 ) = \frac { 1 } { 2 }$.
Q19 Discrete Probability Distributions Proof of Distributional Properties or Symmetry View
Let $X$ and $Y$ be two independent real random variables, each following the distribution $\mathcal { R }$ (where $X ( \Omega ) = \{ - 1,1 \}$, $\mathbb { P } ( X = - 1 ) = \mathbb { P } ( X = 1 ) = \frac { 1 } { 2 }$). Determine the distribution of their product $X Y$.
Q20 Discrete Random Variables Expectation and Variance of Sums of Independent Variables View
Let $m _ { i , j } ( 1 \leqslant i , j \leqslant n )$ be $n ^ { 2 }$ real random variables that are mutually independent, all following the distribution $\mathcal { R }$. The matrix random variable $M _ { n } = \left( m _ { i , j } \right) _ { 1 \leqslant i , j \leqslant n }$ takes values in $\mathcal { V } _ { n , n }$. We set $\tau _ { n } = \operatorname { tr } \left( M _ { n } \right)$.
Calculate the expectation and the variance of the variable $\tau _ { n }$.
Q21 Discrete Random Variables Expectation of a Function of a Discrete Random Variable View
Let $m _ { i , j } ( 1 \leqslant i , j \leqslant n )$ be $n ^ { 2 }$ real random variables that are mutually independent, all following the distribution $\mathcal { R }$. The matrix random variable $M _ { n } = \left( m _ { i , j } \right) _ { 1 \leqslant i , j \leqslant n }$ takes values in $\mathcal { V } _ { n , n }$. We set $\delta _ { n } = \operatorname { det } \left( M _ { n } \right)$.
Calculate the expectation of the variable $\delta _ { n }$.
Q22 Discrete Random Variables Expectation and Variance via Combinatorial Counting View
Let $m _ { i , j } ( 1 \leqslant i , j \leqslant n )$ be $n ^ { 2 }$ real random variables that are mutually independent, all following the distribution $\mathcal { R }$. The matrix random variable $M _ { n } = \left( m _ { i , j } \right) _ { 1 \leqslant i , j \leqslant n }$ takes values in $\mathcal { V } _ { n , n }$. We set $\delta _ { n } = \operatorname { det } \left( M _ { n } \right)$.
Prove that the variance of the variable $\delta _ { n }$ is equal to $n!$
One may expand $\delta _ { n }$ along a row and reason by induction.
Q23 Discrete Probability Distributions Probability Computation for Compound or Multi-Stage Random Experiments View
In the particular case $n = 2$, $m _ { 11 } , m _ { 12 } , m _ { 21 }$ and $m _ { 22 }$ are four real random variables, mutually independent, all following the distribution $\mathcal { R }$ and $M _ { 2 } = \left( \begin{array} { l l } m _ { 11 } & m _ { 12 } \\ m _ { 21 } & m _ { 22 } \end{array} \right)$.
Calculate the probability of the event $M _ { 2 } \in \mathcal { N } _ { 2 }$.
Q24 Discrete Probability Distributions Probability Computation for Compound or Multi-Stage Random Experiments View
In the particular case $n = 2$, $m _ { 11 } , m _ { 12 } , m _ { 21 }$ and $m _ { 22 }$ are four real random variables, mutually independent, all following the distribution $\mathcal { R }$ and $M _ { 2 } = \left( \begin{array} { l l } m _ { 11 } & m _ { 12 } \\ m _ { 21 } & m _ { 22 } \end{array} \right)$.
Calculate the probability of the event $M _ { 2 } \in \mathcal { G } \ell _ { 2 } ( \mathbb { R } )$.
Q25 Discrete Probability Distributions Probability Computation for Compound or Multi-Stage Random Experiments View
We consider $2n$ real random variables $c _ { 1 } , c _ { 2 } , \ldots , c _ { n }$ and $c _ { 1 } ^ { \prime } , c _ { 2 } ^ { \prime } , \ldots , c _ { n } ^ { \prime }$ that are mutually independent, all following the distribution $\mathcal { R }$.
Let $\left( \varepsilon _ { 1 } , \ldots , \varepsilon _ { n } \right) \in \{ - 1,1 \} ^ { n }$. Calculate $\mathbb { P } \left( \left( c _ { 1 } = \varepsilon _ { 1 } \right) \cap \cdots \cap \left( c _ { n } = \varepsilon _ { n } \right) \right)$.
Q26 Discrete Probability Distributions Proof of Distributional Properties or Symmetry View
We consider $2n$ real random variables $c _ { 1 } , c _ { 2 } , \ldots , c _ { n }$ and $c _ { 1 } ^ { \prime } , c _ { 2 } ^ { \prime } , \ldots , c _ { n } ^ { \prime }$ that are mutually independent, all following the distribution $\mathcal { R }$. We consider the random column matrices $C = \left( \begin{array} { c } c _ { 1 } \\ \vdots \\ c _ { n } \end{array} \right)$ and $C ^ { \prime } = \left( \begin{array} { c } c _ { 1 } ^ { \prime } \\ \vdots \\ c _ { n } ^ { \prime } \end{array} \right)$.
Prove that, for all $\omega \in \Omega$, the family $\left( C ( \omega ) , C ^ { \prime } ( \omega ) \right)$ is linearly dependent if and only if there exists $\varepsilon \in \{ - 1,1 \}$ such that $C ^ { \prime } ( \omega ) = \varepsilon C ( \omega )$.
Q27 Discrete Probability Distributions Proof of Distributional Properties or Symmetry View
We consider $2n$ real random variables $c _ { 1 } , c _ { 2 } , \ldots , c _ { n }$ and $c _ { 1 } ^ { \prime } , c _ { 2 } ^ { \prime } , \ldots , c _ { n } ^ { \prime }$ that are mutually independent, all following the distribution $\mathcal { R }$. We consider the random column matrices $C = \left( \begin{array} { c } c _ { 1 } \\ \vdots \\ c _ { n } \end{array} \right)$ and $C ^ { \prime } = \left( \begin{array} { c } c _ { 1 } ^ { \prime } \\ \vdots \\ c _ { n } ^ { \prime } \end{array} \right)$.
Deduce $\mathbb { P } \left( \left( C , C ^ { \prime } \right) \text { is linearly dependent} \right)$.
Q28 Probability Definitions Event Expression and Partition View
We recall that $m _ { i , j } ( 1 \leqslant i , j \leqslant n )$ are $n ^ { 2 }$ real random variables that are mutually independent, all following the distribution $\mathcal { R }$, that $M _ { n } = \left( m _ { i , j } \right) _ { 1 \leqslant i , j \leqslant n }$ is the random matrix taking values in $\mathcal { V } _ { n , n }$ and we denote $$C _ { 1 } = \left( \begin{array} { c } m _ { 11 } \\ \vdots \\ m _ { n 1 } \end{array} \right) , \ldots , C _ { n } = \left( \begin{array} { c } m _ { 1 n } \\ \vdots \\ m _ { n n } \end{array} \right)$$ the random variables taking values in $\mathcal { V } _ { n , 1 }$ constituted by the columns of the matrix $M _ { n }$.
For all $j \in \llbracket 1 , n - 1 \rrbracket$, we denote by $R _ { j }$ the event $$\left( C _ { 1 } , \ldots , C _ { j } \right) \text { is linearly independent and } C _ { j + 1 } \in \operatorname { Vect } \left( C _ { 1 } , \ldots , C _ { j } \right)$$ and $R _ { n }$ the event $$\left( C _ { 1 } , \ldots , C _ { n } \right) \text { is linearly independent.}$$
Show that $( R _ { 1 } , \ldots , R _ { n } )$ is a complete system of events.
Q29 Probability Definitions Proof of a Probability Identity or Inequality View
With the notation of question 28, show that $$\mathbb { P } \left( M \notin \mathcal { G } \ell _ { n } ( \mathbb { R } ) \right) \leqslant \sum _ { j = 1 } ^ { n - 1 } \mathbb { P } \left( C _ { j + 1 } \in \operatorname { Vect } \left( C _ { 1 } , \ldots , C _ { j } \right) \right) .$$
Q30 Probability Definitions Conditional Probability and Bayes' Theorem View
With the notation of question 28, justify that, for all $j \in \llbracket 1 , n - 1 \rrbracket$, $$\mathbb { P } \left( C _ { j + 1 } \in \operatorname { Vect } \left( C _ { 1 } , \ldots , C _ { j } \right) \right) = \sum _ { \left( v _ { 1 } , \ldots , v _ { j } \right) \in \mathcal { V } _ { n , 1 } ^ { j } } \mathbb { P } \left( C _ { j + 1 } \in \operatorname { Vect } \left( v _ { 1 } , \ldots , v _ { j } \right) \right) \mathbb { P } \left( \left( C _ { 1 } = v _ { 1 } \right) \cap \cdots \cap \left( C _ { j } = v _ { j } \right) \right) .$$
Q31 Probability Definitions Proof of a Probability Identity or Inequality View
With the notation of question 28, deduce that, for all $j \in \llbracket 1 , n - 1 \rrbracket$, $$\mathbb { P } \left( C _ { j + 1 } \in \operatorname { Vect } \left( C _ { 1 } , \ldots , C _ { j } \right) \right) \leqslant 2 ^ { j - n } .$$
Q32 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
With the notation of question 28, deduce that $$\mathbb { P } \left( M \in \mathcal { G } \ell _ { n } ( \mathbb { R } ) \right) \geqslant \frac { 1 } { 2 ^ { n - 1 } } .$$
Q36 Proof Bounding or Estimation Proof View
We denote by $I$ a subset of $\mathbb { N }$ having at least two elements and by $u = \left( u _ { i } \right) _ { i \in I }$ a sequence of unit vectors of $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$.
Prove that the real number $$C ( u ) = \sup \left\{ \left| \left\langle u _ { i } \mid u _ { j } \right\rangle \right| , ( i , j ) \in I ^ { 2 } , i \neq j \right\}$$ exists and belongs to the interval $[ 0,1 ]$.
Q37 Hyperbolic functions View
We denote by $I$ a subset of $\mathbb { N }$ having at least two elements and by $u = \left( u _ { i } \right) _ { i \in I }$ a sequence of unit vectors of $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$. $C(u)$ denotes the coherence parameter $C ( u ) = \sup \left\{ \left| \left\langle u _ { i } \mid u _ { j } \right\rangle \right| , ( i , j ) \in I ^ { 2 } , i \neq j \right\}$.
Show that if $C ( u ) = 0$, then the set $\left\{ u _ { i } , i \in I \right\}$ is finite and give an upper bound for its cardinality.
Q38 Hyperbolic functions View
Prove that, for every real number $t$, $\operatorname { ch } ( t ) \leqslant \exp \left( \frac { t ^ { 2 } } { 2 } \right)$.
Q39 Continuous Probability Distributions and Random Variables Characteristic/Moment Generating Function Derivation View
Let $X _ { 1 } , \ldots , X _ { n } , Y _ { 1 } , \ldots , Y _ { n }$ be mutually independent random variables with the same distribution $\mathcal { R }$ (where $X(\Omega) = \{-1,1\}$, $\mathbb{P}(X=-1)=\mathbb{P}(X=1)=\frac{1}{2}$). We define the random vectors $X = \frac { 1 } { \sqrt { n } } \left( X _ { 1 } , \ldots , X _ { n } \right) ^ { \top }$ and $Y = \frac { 1 } { \sqrt { n } } \left( Y _ { 1 } , \ldots , Y _ { n } \right) ^ { \top }$ taking values in $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$.
Prove that, for every real number $t$, $$\mathbb { E } ( \exp ( t \langle X \mid Y \rangle ) ) = \left( \operatorname { ch } \left( \frac { t } { n } \right) \right) ^ { n }$$
Q40 Moment generating functions Upper bound on MGF (sub-Gaussian or exponential inequalities) View
Let $X _ { 1 } , \ldots , X _ { n } , Y _ { 1 } , \ldots , Y _ { n }$ be mutually independent random variables with the same distribution $\mathcal { R }$. We define the random vectors $X = \frac { 1 } { \sqrt { n } } \left( X _ { 1 } , \ldots , X _ { n } \right) ^ { \top }$ and $Y = \frac { 1 } { \sqrt { n } } \left( Y _ { 1 } , \ldots , Y _ { n } \right) ^ { \top }$ taking values in $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$.
Deduce that, for every real number $t$, $$\mathbb { E } ( \exp ( t \langle X \mid Y \rangle ) ) \leqslant \exp \left( \frac { t ^ { 2 } } { 2 n } \right)$$
Q41 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
Let $\sigma$ and $\lambda$ be two strictly positive real numbers and $Z$ a real random variable such that $\exp ( t Z )$ has finite expectation and satisfies $$\forall t \in \mathbb { R } , \quad \mathbb { E } ( \exp ( t Z ) ) \leqslant \exp \left( \frac { \sigma ^ { 2 } t ^ { 2 } } { 2 } \right)$$
By applying Markov's inequality to a suitably chosen random variable, prove that $$\forall t \in \mathbb { R } ^ { + } , \quad \mathbb { P } ( Z \geqslant \lambda ) \leqslant \exp \left( \frac { \sigma ^ { 2 } t ^ { 2 } } { 2 } - \lambda t \right)$$
Q42 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
Let $\sigma$ and $\lambda$ be two strictly positive real numbers and $Z$ a real random variable such that $\exp ( t Z )$ has finite expectation and satisfies $$\forall t \in \mathbb { R } , \quad \mathbb { E } ( \exp ( t Z ) ) \leqslant \exp \left( \frac { \sigma ^ { 2 } t ^ { 2 } } { 2 } \right)$$
Deduce that $$\mathbb { P } ( | Z | \geqslant \lambda ) \leqslant 2 \exp \left( - \frac { \lambda ^ { 2 } } { 2 \sigma ^ { 2 } } \right)$$
Q43 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
Let $X _ { 1 } , \ldots , X _ { n } , Y _ { 1 } , \ldots , Y _ { n }$ be mutually independent random variables with the same distribution $\mathcal { R }$. We define the random vectors $X = \frac { 1 } { \sqrt { n } } \left( X _ { 1 } , \ldots , X _ { n } \right) ^ { \top }$ and $Y = \frac { 1 } { \sqrt { n } } \left( Y _ { 1 } , \ldots , Y _ { n } \right) ^ { \top }$ taking values in $\mathcal { M } _ { n , 1 } ( \mathbb { R } )$.
Prove that $$\mathbb { P } ( | \langle X \mid Y \rangle | \geqslant \varepsilon ) \leqslant 2 \exp \left( - \frac { \varepsilon ^ { 2 } n } { 2 } \right)$$
Q44 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
$N$ being a non-zero natural integer, $\left( X _ { j } ^ { i } \right) _ { 1 \leqslant i \leqslant N , 1 \leqslant j \leqslant n }$ is a family of $n \times N$ mutually independent real random variables with the same distribution $\mathcal { R }$. For every $i \in \llbracket 1 , N \rrbracket$, we set $X ^ { i } = \frac { 1 } { \sqrt { n } } \left( X _ { 1 } ^ { i } , \ldots , X _ { n } ^ { i } \right) ^ { \top }$.
Deduce from the previous questions that $$\mathbb { P } \left( \bigcup _ { 1 \leqslant i < j \leqslant N } \left| \left\langle X ^ { i } \mid X ^ { j } \right\rangle \right| \geqslant \varepsilon \right) \leqslant N ( N - 1 ) \exp \left( - \frac { \varepsilon ^ { 2 } n } { 2 } \right) .$$
Q45 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
$N$ being a non-zero natural integer, $\left( X _ { j } ^ { i } \right) _ { 1 \leqslant i \leqslant N , 1 \leqslant j \leqslant n }$ is a family of $n \times N$ mutually independent real random variables with the same distribution $\mathcal { R }$. For every $i \in \llbracket 1 , N \rrbracket$, we set $X ^ { i } = \frac { 1 } { \sqrt { n } } \left( X _ { 1 } ^ { i } , \ldots , X _ { n } ^ { i } \right) ^ { \top }$.
We assume that $n \geqslant 4 \frac { \ln N } { \varepsilon ^ { 2 } }$. Prove that $$\mathbb { P } \left( \bigcup _ { 1 \leqslant i < j \leqslant N } \left| \left\langle X ^ { i } \mid X ^ { j } \right\rangle \right| \geqslant \varepsilon \right) < 1 .$$
Q46 Continuous Probability Distributions and Random Variables Probability Inequality and Tail Bound Proof View
$N$ being a non-zero natural integer, $\left( X _ { j } ^ { i } \right) _ { 1 \leqslant i \leqslant N , 1 \leqslant j \leqslant n }$ is a family of $n \times N$ mutually independent real random variables with the same distribution $\mathcal { R }$. For every $i \in \llbracket 1 , N \rrbracket$, we set $X ^ { i } = \frac { 1 } { \sqrt { n } } \left( X _ { 1 } ^ { i } , \ldots , X _ { n } ^ { i } \right) ^ { \top }$.
Deduce that, for every natural integer $N$ less than or equal to $\exp \left( \frac { \varepsilon ^ { 2 } n } { 4 } \right)$, there exists a family of $N$ unit vectors of $\mathbb { R } ^ { n }$ whose coherence parameter is bounded by $\varepsilon$.