Hacker News new | past | comments | ask | show | jobs | submit login

In the context of MLE, random has a formal definition. What you describe as poor would be included in the mathematics as a factor outside the deterministic parameters that are modeled. E.g. Y = aFactor1 + bFactor2 + ... + constant + 'poor model correction factor'.

To solve the equation, we have to make assumptions of the poor correction factor. These assumptions about the error generally have some 'mathematically nice' qualities. For example it's not predictable or has a trend relating to any other factors. An concrete example is having a mean of zero. If it had a non-zero mean, it should be accounted in the constant factor of the model.

All these mathematically nice assumptions can be summed up be calling the 'poor model correction' factor as random.




This doesn't make sense to me. Any time you want a reduced order model, you will get error. For example:

- Say I have data which is perfectly sinusoidal, with an dc bias. I can fit a line a to this data, which will approximate the bias (or be exactly the bias if the data is over an integer number of cycles).

- I want to fit a plane to a curved surface

- I want to fit a low order transfer function to a high order system.

- I want to model a system with friction as a system with no friction.

Fitting parameters in all of these situations will result in a non-zero residual. But assuming that is due to randomness is not useful.




Consider applying for YC's Spring batch! Applications are open till Feb 11.

Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: