My Own Personal λ
I updated my main website, dsokol.com with an approximate update frequency. This λ value is the exacted number of updates per day, sampled from the last one year period. Given that my blog has had eight or so updates, over the past 365 days, my future frequency is calculated to be 0.02192. (λ = 8 / 365)
This is not an accurate prediction of frequency, as data for the past 11 of 12 months is missing. In reality, it should be sampled as (8 x 12) / 365, which gives λ = 0.2630 . This is a more accurate model for my blog posting frequency. The same goes for my github frequency, which has only been gathered for the last week or so. My twitter, reddit, stack overflow, zune profile and resume are all accurate, though the reddit number is a lot of guess work.
These λ values should be supplemented with a confidence figure; I’m more confident that my zune and twitter frequencies are around the given λ then my blog or github status. If I were to build a model to simulate my next year of internet activity (which I plan to do, after another month of data is gathered), the lack of background data for these two data series will have to be accounted for.
Why did I even post frequency? I believe it shows certain parts of my character you wouldn’t otherwise see. The fact that my resume is updated very infrequently (λ = 0.00274) shows that I’m pretty happy with my job and not looking. I listen to a lot of music. I’m a pretty steady poster on twitter (unless i spammed certain days, a weakness in my analysis), and I’m decently active in a few communities specific to programming. More importantly, it shows you where to find the most up to date me, which is what the end goal.
