rlabbe / Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Other
16.42k stars 4.15k forks source link

Chapter 03 - central limit theorem #426

Open sedthh opened 2 years ago

sedthh commented 2 years ago

First of all, thank you very much for this learning material, I really wish there were similar tutorials for other engineering areas!

I think the central limit theorem is slightly off in chapter 3: What does this curve mean? Assume we have a thermometer which reads 22°C. No thermometer is perfectly accurate, and so we expect that each reading will be slightly off the actual value. However, a theorem called [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem) states that if we make many measurements that the measurements will be normally distributed. When we look at this chart we can see it is proportional to the probability of the thermometer reading a particular value given the actual temperature of 22°C.

I think it should be something like: ...a theorem called [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem) states that if we make multiple measurements, and average them, these average measurements will be normally distributed...

endolith commented 2 months ago

Duplicate of https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/issues/275