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.

[slideshare id=7249526&w=427&h=356&fb=0&mw=0&mh=0&style=border-bottom: #ccc 0px solid; border-left: #ccc 1px solid; margin-bottom: 5px; border-top: #ccc 1px solid; border-right: #ccc 1px solid&sc=no]

The author Anne Fadiman considers herself a common reader. Who is a common reader ? In her words, The common reader differs from the critic and the scholar. She is worse educated, and nature has not gifted her so generously. She reads for her own pleasures rather than to impart knowledge or correct the opinion of others. Above all, she is guided by an instinct to create for himself, out of whatever odds and ends she can come by , some kind of whole.

Detecting Parkinson’s via a phone call !! http://video.ted.com/assets/player/swf/EmbedPlayer.swf

Was recovering from a brief illness. Tried reading this book just to recover from my drowsy and sullen mood. I found the first part of this book interesting. Given the amount of information overload it often helps us to understand how our brain functions. “How do we use our brains for understanding, deciding, recalling, memorizing and inhibiting information ?” is an important question that we all need to answer, to function efficiently in our lives.