[youtube https://www.youtube.com/watch?v=ChTnwpkCMhg?rel=0]

I liked Daniel Coyle’s “Talent Code” that talks about the importance of “deep practice” in achieving mastery in any field. Not for the message of deep practice as it was already repeated in many books/articles, but for the varied examples in the book. Here comes another book on the same lines by the same author. This book is a collection of thoughts and ideas from author’s field work, packaged as “TIPS” to improve one’s skillset.

Likelihood function is a very useful mathematical object in statistics. With it, you can perform the two main tasks in statistics,i.e. estimation and inference. If you can get the distribution right or the overall structural equation right, you can do all types of stats; univariate stats, multivariate stats , linear models, generalized linear model, mixture modeling, mixed effects model and even non parametric statistics to an extent. All of this can be done from scratch with one math object, “Likelihood function” + pen & paper + a plain vanilla optimization routine.

This graphic novel talks about Steve Jobs and Zen Buddhist priest Kobun, who acted as Jobs’ spiritual guru. Hard core Apple fans might like to know the kind of conversations that Jobs had with Kobun . However I felt the book was pointless. I think it is merely trying to cash in on two aspects, 1) Increasing popularity of graphic novels among adults and 2) Steve Jobs death in Oct 2011.

Stumbled on to an interesting paper that connects Bayesian ideas to Likelihood based inference. Both are related in the sense that Likelihood based Inference can be thought of a Bayesian Inference with uniform/vague prior. However when you get down to estimating and inferring from the data using these two philosophies, the math, the equations you use, the code you need to write are completely different. This paper by Steel talks about whether a hard core Bayesian must accept Likelihood principle or not.