Bayesian Reasoning: Gender Inference from a Specimen Measurement

Imagine that we have a population of something composed of two subset populations that, while distinct from each other, share a common characteristic that can be measured along some kind of scale. Furthermore, let’s assume that each subset population expresses this characteristic with a frequency distribution unique to each. In other words, along the scale of measurement for the characteristic, each subset displays varying levels of the characteristic among its members. Now, we choose a specimen from the larger population in an unbiased manner and measure this characteristic for this specific individual. Are we justified in inferring the subset membership of the specimen based on this measurement alone? Baye’s rule (or theorem), something you may have heard about in this age of exploding data analytics, tells us that we can be so justified as long as we assign a probability (or degree of belief) to our inference. The following discussion provides an interesting way of understanding the process to do this. More importantly, I present how Baye’s theorem helps us overcome a common thinking failure associated with making inferences from an incomplete treatment of all the information we should use. I’ll use a bit of a fanciful example to convey this understanding along with showing the associated calculations in the R programming language.

Suppose we are aliens from another planet conducting scientific research on this strange group of bipedal organisms called humans...



To read the entire discussion go here.

VoyageATL is an online magazine that highlights local small businesses and entrepreneurs and promotes local events. A few days ago they published a small piece on my company, Incite.

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Book trailers

I made the following "movie" trailers for two of my tutorials to play with the idea of making a teaser that didn't attempt to explain anything, but mostly just to have some fun.

It’s Your Move: Creating Valuable Decision Options When You Don’t Know What to Do


Information Espresso: Using Value of Information for Making Clear Decisions (with support from the R programming language)

Bridging the Gap: The Economics of Data Science and Decisions