Thinking About Bioinformatics

As I discussed in a previous post, I want to emulate the admirable clarity and accessibility of Feynman’s Lectures on Physics in my own attempt to write an introductory textbook on information metrics for statistical inference.  Below are my thoughts on how I can apply the lessons that I drew from Feynman in my previous post.

More to the point, I’ve rewritten Chapter 1  What is Inference? based on these lessons.  So now I ask you: is this a genuine improvement?  Note that this is an intro chapter with only the simplest math (some addition and multiplication), so anyone should be able to understand it and critique it!  Please add comments to this post to give your opinion of whether you think the specific changes I outline below improve the chapter, compared with the original version.  I am particularly interested in both whether you think the ideas in my plan are the right direction to pursue, versus whether their actual “reduction to practice” in the new draft chapter works or not.  Above all, tell me how I need to improve my chapter and my writing!

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I just refactored a number of internal aspects of Pygr’s object-relational model, to make use of a new pattern I’m calling “subclass binding”, which I’ll try to explain a bit in this post.  First I’ll try to explain the problem from the viewpoint of a Python programmer.

Object-relational design makes modularity even more difficult than usual. It illustrates a general problem: when you try to combine two different behaviors (e.g. a local Python object and a back-end database) into one object, all sorts of confusion can ensue.

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