A Simple (But Not Too Simple) Intro to Linear Estimators

Author(s): Maxwell’s Demon

Originally published on Towards AI.

Optimally combining prior knowledge with new data

Let’s start with an example. Say a lab technician knows from long-term experience that the lab’s temperature usually hovers around 20 °C. On a particular day, she notices some changes in the environment and decides to measure it. The thermometer, however, is noisy — it can be off by about ±2 °C. She cannot fully trust her prior knowledge, nor can she rely entirely on the new measurement.

This article discusses the concept of linear estimators by using a practical example to illustrate how a lab technician should optimally combine her previous knowledge of ambient temperature with noisy measurements. It emphasizes the theoretical basis for linear estimators, providing a simplified mathematical treatment, and explores their relevance in everyday scenarios, pointing out how the method reflects concepts in Bayesian thinking and can be applied in various contexts such as machine learning and system identification.

Read the full blog for free on Medium.

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Published via Towards AI