Michael Clark
@statsdatasci
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Statistical Philosopher, Brute Empiricist
Ann Arbor
Joined September 2016
📖 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! https://t.co/p2sSd26smc (@CRCPress) https://t.co/rK1yopnkxi (web) #DataScience #MachineLearning #AI
routledge.com
Unlock the Power of Data Science and Machine Learning In this comprehensive guide, we delve into the world of data science, machinelearning, and AI modeling, providing readers with a robust foundat...
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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":
onesixsolutions.com
Gain practical insights into predictive modeling in data science and learn how it helps analyze complex data, inspired by the Models Demystified book.
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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! https://t.co/DnE31gVB6T
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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: https://t.co/DnE31gV3hl. Hope you find it useful, and feedback is appreciated as we continue to work on it!
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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: https://t.co/DnE31gV3hl. Hope you find it useful, and feedback is appreciated as we continue to work on it!
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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).
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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. https://t.co/NiSPShbAl2
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Here are some helpful #guidelines for working with tabular #data
https://t.co/0ABAJkVBrz
#deeplearning #tabulardata
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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
https://t.co/yNn78HE615
https://t.co/ZJj1vrLxmQ
strong.io
Find out how we leverage deep learning and traditional approaches to build robust statistical solutions for complex business problems
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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
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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. https://t.co/mf9NqHWHuI
https://t.co/CFgpS4aeLk
#rstats
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New post demonstrating time series modeling from arima to deep learning with CTA ridership data. Complements @CodyDirks recent post at the @stronganalytics blog ( https://t.co/GbjC9FrG8L).
https://t.co/cwTHtZ8N8d
#rstats #gam #pytorch
strong.io
Check out how predictive modeling can provide an organization as complex as the CTA with valuable information about their daily operations.
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New post on some programming explorations with an eye toward speed and memory efficiency. Hopefully can help others when doing similar operations. https://t.co/ZgzvXgO7TN
#rstats
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Updated my doc on Generalized Additive Models! Cleaned up code, plots, updated tools/packages, added small section on Bayesian GAM, etc. https://t.co/tQvU8UjtWw
#rstats #GAM
m-clark.github.io
An introduction to generalized additive models (GAMs) is provided, with an emphasis on generalization from familiar linear models. It makes extensive use of the mgcv package in R. Discussion includes...
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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. https://t.co/kExhOtnZcW
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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! https://t.co/va9u0YsjcA
#rstats @mcmc_stan
m-clark.github.io
This document provides an introduction to Bayesian data analysis. It is conceptual in nature, but uses the probabilistic programming language Stan for demonstration (and its implementation in R via...
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Doing some minor updates to my mixed model doc, esp. for clarity. Suggestions welcome, so feel free to post an issue on GitHub! https://t.co/EgE2H0cUxt
https://t.co/A9EkiG75OL
m-clark.github.io
This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized...
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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: https://t.co/lMeu186tvh Demo of the double descent phenomenon:
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Here is Part II: https://t.co/0fX3McO31N 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.
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