Real functions Classical inequalities Theorem
Jensen's inequality
Builds on Convex function
Statement
Let be an interval and let be convex. Let , and let with . Then
Remarks
The case is the definition of convexity. Induction on gives the general case.
For a concave function, the inequality is reversed. With this gives the AM–GM inequality.
Mathlib states it as ConvexOn.map_sum_le.
History
Johan Jensen proved the inequality in 1906.
Reading path
To understand this page from the ground up, read these 2 pages in order. The foundations come first.
Show the path as a graph
- no evidence level
- Convex function definition
- Jensen's inequality statement
Where this leads
17 pages build on this one.
- Doob's upcrossing inequality
- Doob's martingale inequality
- Consistency of the maximum likelihood estimator
- Log-modulus of a holomorphic function is subharmonic
- Asymptotic normality of the maximum likelihood estimator
- Wald's equation
- Optional stopping theorem
- Convex functions of subharmonic functions
- Submartingale
- Doob's martingale convergence theorems
- Expectation–maximization algorithm
- Doob–Meyer decomposition theorem
- Hadamard three-circle theorem for subharmonic functions
- Jensen inequality as a functional inequality
- Skorokhod's embedding theorem
- Generalized mean inequality
- Doob decomposition theorem
Further reading
About this page
| Tag | 0RWMRTPC87 |
|---|---|
| URI | m2://0RWMRTPC87 |
| Kind | statement, theorem |
| Writing | W1 stated |
| MSC | 26D15 |
| Status | This copy of the site has no build data. Run m2 status 0RWMRTPC87. |
| Uses | Convex function |
| Same as |
Markdown · JSON · Edit on GitHub
What this page needs for W2
- edge about out: the concepts that the statement is about (0 of 1)
- edge proves in: at least one proof node (0 of 1)
- edge instance_of or illustrates in: one example that shows the statement at work (0 of 1)
- explanation L2 (0 of 1)
The writing levels say how complete the text of a page is. They do not measure its truth.
What links here
Used by
- Consistency of the maximum likelihood estimator
- Convex functions of subharmonic functions
- Submartingale
- Expectation–maximization algorithm
- Jensen inequality as a functional inequality
- Generalized mean inequality