(c) 2016 Justin Bois. This work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.
This tutorial exercise was generated from an Jupyter notebook. You can download the notebook here. Use this downloaded Jupyter notebook to fill out your responses.
When performing parameter estimation by optimization, after we find the MAP, why do we locally approximate the posterior near the MAP as Gaussian?
You may have heard that curve fitting involved "minimizing the sum of the square of the residuals." If indeed finding the MAP is equivalent to minimizing the sum of the square of the residuals, what underlying assumptions are there in the statistical model?
How would you expect each of the following to be distributed?
a) The amount of time between repressor-operator binding events.
b) The number of times a repressor binds its operator in a given hour.
c) The amount of time (in total minute of baseball played) between no-hitters in Major League Baseball.
d) The number of no-hitters in a Major League Baseball season.
To answer this question, try to match these biological stories to the stories of named distributions. For those of you not familiar with baseball, a no-hitter is a game in which a team concedes no hits to the opposing team. There have only been a few hundred no-hitters in over 200,000 MLB games.