The author delves into Jensen’s Inequality, exploring its implications and practical applications in real-world scenarios. The inequality states that the expected value of a function is greater than or equal to the function applied to the expected value of the input when the function is convex. The author provides intuitive examples, such as traffic flow and stock options, to demonstrate how Jensen’s Inequality guides accurate predictions in non-linear domains. The article highlights the significance of considering the type of function applied to inputs when making estimations, emphasizing that exponential dynamics can skew predictions. A thought-provoking analysis of predicting outcomes based on average inputs and the impact of convexity and concavity on results is offered.
https://blog.moontower.ai/jensens-inequality-as-an-intuition-tool/