This is a really interesting post. I have to say the data science team at Stitchfix are clearly doing really good, applied work that is central to their business. It's so cool to see.
Here's a tip for any R users who read through the code and (like me) is pained by repetition. Instead of using
with the added benefit of installing missing packages (I find this especially useful because my school's computer lab deletes user-installed packages weekly)
In terms of managing packages in session, readability and keystrokes, I think it definitely wins out over successive library calls. It's common to have >5 packages in any one script, especially if you avoid base R like many do.
I understand where you're coming from, but these two lines are exactly 'taking a list of libraries for import'.
packages is a character vector of package names, lapply is by definition 'list apply'. We're taking a list of packages and applying the library function on them.
This seems complicated if you're not used to it but R is a functional language. Approaching R from this perspective makes it a powerful, flexible.
I work with time series data every day in the domain of commercial real estate. One of my constant struggles is to extract an underlying long-term trend from the real estate cycle. I would love to try this here.
Can anyone suggest some Bayesian learning resources for a non-statistician?
"Statistical Rethinking" by Richard McElreath is really nice, light on maths but heavy on insights. He uses the book in a course and there are recordings of the lectures available on youtube.
Edit: neither this book nor Kruschke's are going to help you with time series in particular. Bayesian time series methods are based on state space models and are relatively complex (http://www.eurasip.org/Seminars/Tutorials/EUSIPCO2014%20Tuto...). You might want to read some introductory text on time series covering state space models first.
It gives a gentle but through introduction from first principles; lots of good intuition and 'why'.
It works well with
"Probabilistic Programming & Bayesian Methods for Hackers"
also mentioned, but I'd start with this. It is much more accessible than many other introductory books, IMO.
Really nice story. All the Bayesian stuff now gets swamped by big data method. There must be a time machine learning people will come back to the Bayesian womb. :-)
I'm currently using it to define priors on measure spaces. I think it's awesome to have so few abstractions in a discipline and be able to do inference anyway. I'd definitely recommend to look into Dirichlet Processes if you haven't before. It's a nice entry point.
I was very surprised when I went to the root domain to see what stitchfix is. Completely not what I was expecting! I guess my model didn't predict a clothes delivery website to be writing about cool stuff like this. But now a bayesian update to this belief is in order :)
It is ironic that your off-topic comment lead to down-votes, reducing the contrast of your comment. I can't imagine the nightmare you must be living right now.
This book is extremely practical - I would definitely recommend it for doing actual time series analysis. That said, it's not a learn time series book. It's a do basic time series in R book.
This was very interesting to read, especially as I'm currently taking a course in time series analysis. The course doesn't touch on any Bayesian treatment of the topic at all, and barely goes beyond non-stationary models (e.g. ARIMA.)
https://pymc-devs.github.io/pymc3/stochastic_volatility/