Analyze shared links from Learning Bayesian Statistics

The social network this podcast cites the most is Twitter (72 unique links). Another source it references often is Github (86 links). The most cited link overall is a Other link, mentioned 287 times. Everything above comes straight from the public descriptions the creators wrote themselves. We don't add, remove, or assume anything.

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The table below shows each link, how many times Learning Bayesian Statistics has used it, and which other podcasts have cited the same link.

Link	#	Other Shows
https://bababrinkman.com/	287
https://www.patreon.com/learnbayesstats	237
https://www.pymc-labs.io/	95
https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf	93
https://www.intuitivebayes.com/	57
https://topmate.io/alex_andorra	56
https://topmate.io/alex_andorra/503302	31
https://topmate.io/alex_andorra/1011122	31
https://docs.pymc.io/	14
http://twitter.com/LearnBayesStats	13
https://mc-stan.org/	11
https://turing.ml/dev/	7
https://tidelift.com/	6
https://arviz-devs.github.io/arviz/	6
https://pyro.ai/	6
https://forms.gle/YAT5wZj9NbFyKykB8	5
https://pymc-devs.github.io/symbolic-pymc/	4
https://www.youtube.com/watch?v=ZaZwCcrJlik	4
https://learnbayesstats.com/episode/101-black-holes-collisions-gravitational-waves-ligo-experts-christopher-berry-john-veitch/	4
https://www.youtube.com/watch?v=vVqZ0WWXX7g	4
https://facebook.github.io/prophet/	4
https://www.gen.dev/	4
https://www.youtube.com/watch?v=lnq5ZPlup0E	4
https://www.youtube.com/watch?v=pRaT6FLF7A8	4
https://learnbayesstats.com/episode/96-pharma-models-sports-analytics-stan-news-daniel-lee/	4
https://learnbayesstats.com/episode/99-exploring-quantum-physics-bayesian-stats-chris-ferrie/	4
https://learnbayesstats.com/episode/97-probably-overthinking-statistical-paradoxes-allen-downey/	4
https://www.youtube.com/watch?v=ZG3H0xxCXTQ	4
https://www.tensorflow.org/probability/	4
https://learnbayesstats.com/episode/100-reactive-message-passing-automated-inference-in-julia-dmitry-bagaev/	4
https://www.learnbayesstats.com/buy-think-bayes	4
https://www.youtube.com/watch?v=KgesIe3hTe0	4
https://colindcarroll.com/	4
http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020	4
https://bayesball.github.io/	4
https://github.com/cscherrer/Soss.jl	4
https://learnbayesstats.com/episode/98-fusing-statistical-physics-machine-learning-adaptive-mcmc-marylou-gabrie/	4
http://www.stat.columbia.edu/~gelman/	4
https://statmodeling.stat.columbia.edu/	4
https://twiecki.io/blog/2019/01/14/supply_chain/	4
https://twitter.com/statmodeling	4
https://paul-buerkner.github.io/brms/	4
https://www.coursera.org/learn/improving-statistical-questions	3
https://scholar.google.nl/citations?user=ZbqYyrsAAAAJ&amp	3
https://opennessinitiative.org/	3
http://daniellakens.blogspot.com/	3
https://twitter.com/junpenglao	3
https://bambinos.github.io/bambi/	3
https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/	3
https://github.com/Lakens	3

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