Video Timestamps

Why We Are Adding Timestamps

:stopwatch: We are adding timestamps to the PyMC YouTube videos. When timestamps are available:

1. It makes it easy for viewers to get to the part in the video they are interested in.
2. It also helps potential viewers find the video based on their search terms.

:pray: Your helpful contribution is greatly appreciated!!

Instructions

1. Watch the video and add descriptive timestamps to the issue you opened.
2. To submit your timestamps, there are two options:

• Open a new issue with the videos title and add the timestamps information there.
• Create a pull request with the timestamps in this folder: videos-list

3. Below is a preliminary list. The video descriptions can first be checked to see if the timestamps are there or not.
4. When you pick a video to work work, please open an issue, so two people are not working on the same video (avoid duplicating work).
5. Please pick one video at a time.
6. In the description of your PR, include this text:
   a) For Closes #13, you should put it in the number of any issues opened related to the video:

See Example: PR #14

Pull Request title: Add timestamps for video 3: Violeta keynote

### Reference
Towards #11 
Closes #13

### Description
Add timestamps for video 3: Violeta keynote

### Event
#DataUmbrellaPyMCSprint

Videos

References

• Videos are here: PyMC Dev videos
• Timestamps can be added to the files here: videos-list

Videos that need timestamps:

• 36 Danh Phan: Gaussian Processes | video | Add timestamps here: 36-danh_gp.md (@HangenYuu)
• 35 Dante Gates: Power of Bayes in Industry | video | Add timestamps here: 35-dante_bayes.md (@Mr-Imperium)
• 34 Ricardo Vieira: PyMC, Aesara, Aeppl | video | Add timestamps here: 34-ricardo_aesara.md (@reshamas)
• 33 Thomas Wiecki, Bayesian Workflow, Part 1 (done by @AnaRita93)
• 32 Michael Osthege & Laura Helleckes, Calibr8 (done by @IndigoWizard)
• 31 Sayam Kumar, variational inference
• 30 Quan Nguyen, Bayesian ML (done by @yyyyuans & @AnaRita93)
• 29 Luciano Paz, Posterior prediction sampling (done by @zbraiterman)
• 28 Evdoxia Taka, Bayesian visualization (done by @bsenst)
• 27 Cameron Davidson, Microbial cell counting (done by @bsenst)
• 26 Ali Akbar, Journey in ML (done by @bsenst)
• 25 Chris Fonnesbeck, Keynote (done by @bsenst)
• 24 Agustina Arroyuelo, Glycan 3D (done by @Rohanfizz)
• 23 Elizaveta Semenova, Toxicity prediction
• 22 Nicoleta Spînu, Causal network
• 21 Max Sklar, What is probability? (done by bsenst)
• 20 Alex Andorra, Multinomal regression
• 19 Matthijs Brouns, Hierarchical
• 18 Dan Foreman-Mackey, PyMC extension
• 17 Ravin Kumar, Welcome (done by @bsenst)
• 16 Tushar Chandra, Pokemon GO
• 15 Ivan Tashchuk, Partial differential equations
• 14 Ruben Mak, Hierachial models
• 13 Pedro and Osvaldo , Sequential Monte Carlo (@hivana)
• 12 Michael Johns, Mixed Modeling (@BerylKanali)
• 11 Mo Akhshik, Novel bayesian models (@zbraiterman)
• 10 Rob Zinkov, Model checking (@yyyyuans)
• 09 Junpeng Lao, Missing multivariate
• 08 Vincent Warmerdam, Priors
• 07 Thomas Wiecki, Bayesian workflow part 2 (@AnaRita93 & @yyyyuans)
• 06 Allen Downey, Bayesian zig zag
• 05 Dario and Hessam, Models in chemistry (done by @SangamSwadiK)
• 04 Aki Vehrati, Inference diagnostics (done by @bsenst)
• 03 Viola Priesemann, SARS-COV-2 spread inferring (done by bsenst)
• 02 Tim D, Mikkel L and Grigorios M, MLDA multilevel sampler (@toraigeki)
• 01 Interviews with PyMC Team members + Community (Tidelift) (done by @reshamas)