The Thinking About Bioinformatics Blog is a collection of Thoughts on Information Evolution, Systems, and Tools. It is written by Christopher Lee who is a Professor in the Dept. of Chemistry and Biochemistry at UCLA. Here is a clip from his about page:
Here are some of the kinds of topics I hope to write about:
- the general information metric hypothesis: the notion that there is a general measure of information that in some sense is the “answer to all questions” in science, i.e. the best experiment to do is the one that maximizes the information yield and rate of production. There are many interesting arguments both for and against this idea, so in my view this is a great place to put everything we think we know about “information” to a rather searching test. Much of what I’m going to post here focuses on the idea that we can only understand information in Bayesian terms, i.e. as a hidden property of observable variables.
- biology as information and information as biology: there are striking parallels between the population genetic theory of evolution and the theory of statistical inference. For example, the equation for the evolution over time `t` of an asexual haploid population `p_i(t)` under natural selection `W_i^t` is identical to Bayes Law:
Thanks Christopher for using the Beach House theme!