Statistical Philosopher, Brute Empiricist
Ann Arbor
Joined September 2016
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📖 Excited to announce my book, Models Demystified, is out Friday! It covers concepts from stats to deep learning, & other topics like uncertainty, causal inference & more!
tinyurl.com/book-at-crc (@CRCPress)
tinyurl.com/book-web (web)
#DataScience #MachineLearning #AI
Michael Clark retweeted
From classic techniques to cutting-edge machine learning, data science models help uncover patterns and power smarter predictions. Check out our latest blog post for key insights from Michael Clark's book "Models Demystified": bit.ly/4hIwnic
Hey folks! Been a while since I posted about it, but our book on practical data science modeling has come a long way since then. Would love to hear some thoughts on GitHub or here. Hopefully we'll get it done soon and out on @CRCPress in the near future!
m-clark.github.io/book-of-mo…
Michael Clark retweeted
I hope @statsdatasci doesn't mind me announcing that this book will be published by Chapman and Hall/CRC likely in 2024.
I've been putting together a book on modeling that I hope will appeal to a wide range of audiences, with examples in Python/R.
You can check out the in-progress work at: m-clark.github.io/book-of-mo…. Hope you find it useful, and feedback is appreciated as we continue to work on it!
I've been putting together a book on modeling that I hope will appeal to a wide range of audiences, with examples in Python/R.
You can check out the in-progress work at: m-clark.github.io/book-of-mo…. Hope you find it useful, and feedback is appreciated as we continue to work on it!
Been a while, but here's my new post on marketing/media mix models at the @stronganalytics blog. Those used to time series and mixed modeling might find this an interesting application.
strong.io/blog/marketing-opt…
There are good tools in #rstats (e.g. Robyn) and #python (lightweightmmm), but as noted in the article, you often will just have to roll your own (e.g. via @mcmc_stan or #numpyro).
Michael Clark retweeted
Here are some helpful #guidelines for working with tabular #data buff.ly/3ZjT3fI #deeplearning #tabulardata
The @stronganalytics blog has really come alive this year and is now providing near weekly posts on topics in data science, #AI, and related. I also contributed a few weeks ago! 😄 #DataScience
strong.io/blog/
strong.io/blog/deep-learning…
After seeing some frustrating pks at the world cup, I used some data found on kaggle, #brms and #tidybayes to get some posterior predictive distributions for probability of goal by location/zone kicked. Fun data to play with! #WorldCup2022
Updated my {mixedup} 📦. If you switch among multiple mixed model packages but would like a similar set of functions/tidy results whichever one you're using, then this may be of use to you.
github.com/m-clark/mixedup/
m-clark.github.io/mixedup/
#rstats
New post demonstrating time series modeling from arima to deep learning with CTA ridership data. Complements @CodyDirks recent post at the @stronganalytics blog (
strong.io/blog/forecasting-p…).
m-clark.github.io/posts/2021…
#rstats #gam #pytorch
New post on some programming explorations with an eye toward speed and memory efficiency. Hopefully can help others when doing similar operations.
m-clark.github.io/posts/2022…
#rstats
Updated my doc on Generalized Additive Models! Cleaned up code, plots, updated tools/packages, added small section on Bayesian GAM, etc.
m-clark.github.io/generalize…
#rstats #GAM
New post summarizing some additional recent articles of applications of DL models for tabular data. Includes a summary of all findings reviewed in this and a previous post.
m-clark.github.io/posts/2022…
Another document update, this time to my Bayesian introduction with Stan as the backdrop. Clarified some text and (very old) code, along with a little bit of content update here and there. Enjoy!
m-clark.github.io/bayesian-b…
#rstats @mcmc_stan
Doing some minor updates to my mixed model doc, esp. for clarity. Suggestions welcome, so feel free to post an issue on GitHub!
github.com/m-clark/mixed-mod…
m-clark.github.io/mixed-mode…
Neglected to note a couple blog posts last year (better late than never?):
Summary of articles exploring the effectiveness of deep learning vs. other methods (esp. boosting) for tabular data: m-clark.github.io/posts/2021…
Demo of the double descent phenomenon: m-clark.github.io/posts/2021…
Born from common client questions, I recently posted some help for applied users get past those those warnings, errors, and other complexities in their Bayesian efforts, and feel better about their results. Hope it helps!
m-clark.github.io/posts/2021…
#rstats #brms #mcstan
Here is Part II:
m-clark.github.io/posts/2021…
Aside from that, I learned that multi-part posts are not a good idea, and neither is putting posts off for months at a time.