Bhatia–Davis inequality

In mathematics, the Bhatia–Davis inequality, named after Rajendra Bhatia and Chandler Davis, is an upper bound on the variance σ² of any bounded probability distribution on the real line.

Statement

Let m and M be the lower and upper bounds, respectively, for a set of real numbers a₁, ..., a_n , with a particular probability distribution. Let μ be the expected value of this distribution.

Then the Bhatia–Davis inequality states:

$\sigma ^{2}\leq (M-\mu )(\mu -m).\,$

Equality holds if and only if every a_j in the set of values is equal either to M or to m.

Proof

Since $m\leq A\leq M$,

$0\leq \mathbb {E} [(M-A)(A-m)]=-\mathbb {E} [A^{2}]-mM+(m+M)\mu$.

Thus,

$\sigma ^{2}=\mathbb {E} [A^{2}]-\mu ^{2}\leq -mM+(m+M)\mu -\mu ^{2}=(M-\mu )(\mu -m)$.

Extensions of the Bhatia–Davis inequality

If $\Phi$ is a positive and unital linear mapping of a C* -algebra ${\mathcal {A}}$ into a C* -algebra ${\mathcal {B}}$, and A is a self-adjoint element of ${\mathcal {A}}$ satisfying m $\leq$ A $\leq$ M, then:

$\Phi (A^{2})-(\Phi A)^{2}\leq (M-\Phi A)(\Phi A-m)$.

If ${\mathit {X}}$ is a discrete random variable such that

$P(X=x_{i})=p_{i},$ where $i=1,...,n$, then:

$s_{p}^{2}=\sum _{1}^{n}p_{i}x_{i}^{2}-(\sum _{1}^{n}p_{i}x_{i})^{2}\leq (M-\sum _{1}^{n}p_{i}x_{i})(\sum _{1}^{n}p_{i}x_{i}-m)$,

where $0\leq p_{i}\leq 1$ and $\sum _{1}^{n}p_{i}=1$.

Comparisons to other inequalities

The Bhatia–Davis inequality is stronger than Popoviciu's inequality on variances (note, however, that Popoviciu's inequality does not require knowledge of the expectation or mean), as can be seen from the conditions for equality. Equality holds in Popoviciu's inequality if and only if half of the a_j are equal to the upper bounds and half of the a_j are equal to the lower bounds. Additionally, Sharma has made further refinements on the Bhatia–Davis inequality.

See also

• Cramér–Rao bound
• Chapman–Robbins bound
• Popoviciu's inequality on variances