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Jensen's inequality

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Jensen's inequality is a fundamental principle in mathematics that relates the value of a convex function of an integral to the integral of that convex function. In simpler terms, it describes a predictable relationship between a curve and the averages of values on that curve. This inequality is essential because it provides a way to compare two different methods of calculation. One method involves finding an average first and then applying a function to it. The second method applies the function to individual values first and then calculates their average.

ConvexFunction
ConvexFunction

This rule is used across many scientific fields, including probability theory, information theory, and statistical physics.

To understand the mechanism, we must first define a convex function. A function is convex if a straight line drawn between any two points on its graph stays above or on the graph itself. When we apply a convex function to a set of numbers, the inequality states that the function of the mean is less than or equal to the mean of the function.

Jensen's Inequality Proof Without Words
Jensen's Inequality Proof Without Words

If the function is concave, which is the opposite of convex, the inequality is reversed. The difference between the two sides of the inequality is known as the Jensen gap. If the function is a straight line, the two sides are equal, and there is no gap.

There are several ways to express this inequality depending on the mathematical context. The finite form uses specific numbers and positive weights to show how weighted averages behave. In this version, the inequality applies to any real convex function and any set of numbers in its domain. The measure-theoretic form is more advanced and uses the language of integration. It applies to non-negative integrable functions and probability spaces.

Jensen graph
Jensen graph

Finally, the probabilistic form is widely used in statistics. It describes a random variable X and a convex function, stating that the expected value of the function of X is greater than or equal to the function of the expected value of X.

The history of this discovery involves several important mathematicians. The inequality is named after Johan Jensen, a Danish mathematician. He provided a general proof for the inequality in 1906. His work was significant because it was much more broad than previous versions. Before Jensen, Otto Hölder proved a similar inequality in 1889. However, Hölder's proof only worked for doubly-differentiable functions, which are specific types of smooth curves. Jensen's contribution allowed the rule to apply to a much wider variety of mathematical situations.

The significance of Jensen's inequality is seen in its many specific applications. In information theory, it leads to Gibbs' inequality. This result shows that the average message length is minimized when codes are assigned based on true probabilities. It also defines the Kullback–Leibler divergence, which measures the difference between two probability distributions. In statistical physics, the inequality is vital when working with exponential functions. It helps scientists understand how systems behave under different probability distributions.

Another notable application is the Rao–Blackwell theorem in statistics. This theorem uses the conditional version of Jensen's inequality to improve estimators. If a researcher is trying to estimate an unknown parameter, they can use a sufficient statistic to find a better estimate. This new estimate has a smaller expected loss, making it more accurate. The theorem shows how we can use known information to reduce uncertainty in our calculations. This makes the inequality a powerful tool for anyone working with data and observations.

Jensen's inequality also connects to how humans perceive value and risk. In economics, it can describe risk aversion. This occurs when people prefer a certain outcome over a gamble with a higher average value. This preference is linked to the idea of declining marginal utility. The inequality provides a formal way to model these human behaviors mathematically. By connecting abstract curves to real-world decisions, Jensen's inequality shows how mathematics helps us understand the logic of the world.

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File:Jensen graph.svg
Jensen graph.svg
File:Jensen's Inequality Proof Without Words.png
Jensen's Inequality Proof Without Words.png
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