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rmcelreath
statrethinking_winter2019
Learning & Reference2019–2020
rmcelreath / statrethinking_winter2019

statrethinking_winter2019

Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019

Learning & ReferenceScience & Engineering
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0Explicit recommenders
2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2019-06-05 10:28:17 UTC to 2020-05-06 20:33:35 UTC.

012019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJun 20192019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsNov 20192019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC110
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC000
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC000
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC101

What Hacker News says

3 sources
Jabbermonkey·Dec 12, 2019
If you're genuinely eager to learn probabilistic programming then by far the best resource I've found is a book called Statistical Rethinking by Richard McElreath. His (almost finished) draft for the second edition is up here: http://xcelab.net/rmpubs/sr2/statisticalrethinking2_08dec19.... Dr. McElreath also posts his lectures on youtube. The R code in the book and his lectures use a library/package he wrote which provides a wrapper to simplify building Stan models. The code has also been translated to PyMC3 on Python. Code, slides, lecture videos are all referenced here: https://github.com/rmcelreath/statrethinking_winter2019 It might look like a huge amount of content but this course leads you very gently through key concepts, keeping the mathematics to a minimum. Don't be put off if you don't know the R language. The concepts are more
Read the rest
important than the programming language and the code examples are kept simple. If you make it through Statistical Rethinking then you might consider picking up Doing Bayesian Data Analysis by John Kruschke (a.k.a. the puppies book). I've found DBDA to be heavier going than SR but Kruschke takes a different approach to McElreath which can be useful if you get stuck on a concept, need more detail or just want a different angle on the subject.
In PPX: Probabilistic Programming EXecution Protocol and API Based on Flatbuffers

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