Closed VectorPosse closed 5 years ago
Sean, that would be awesome! Could I tentatively put you down for November this year? And as it gets closer I coordinate more with you?
Sure, November sounds good.
Hello @VectorPosse! We are working on planning the November meeting. What day would work for you to present on Stan? We usually meet during the week at lunchtime and could possibly meet downtown or in Murray at IMC, if one works for you? Do you have preferences? Something the week of Nov 5 or 12? Tues/Wed/Thurs?
Hi Julia,
Monday through Thursday I have class right up until noon. Would a 12:30 starting time work for people? On Tuesdays and Thursdays, I have class at 2:00, so I'd have to get back (but that should be possible). Mondays and Wednesdays are often a little more flexible. There are also a few Fridays I could make work at 12:00. So what's the priority for the group, a noon start time, or the day of the week?
@VectorPosse, whatever works best for you. I don't think starting a little later or on a different day than Thursday would have much of an impact. Thanks for your willingness to present.
The best day/time for me would be Friday, November 16 at 12:00 p.m.
If people feel Friday wouldn't get the same draw, my second-best day/time would be Wednesday, November 14 at 12:30 p.m.
I'm open to trying a Friday. What do you think @nielsenmarkus11?
I think we can give Friday a try... I'll try and set something up at Neumont if I can. If not we'll try IHC. Thanks @VectorPosse!
@VectorPosse, I set it up at Neumont Room #209 for Wednesday at 12:30pm. Is this still okay?
Which day?
November 14th.
Yep, that works great. I'm looking forward to it.
Awesome. We are looking forward to it too. Thank you!
Do you have a Title/Summary you would like me to post? Also, a sentence or two bio?
Sure, give me a day or two to put that together.
Okay here we go:
Title: Bayesian modeling with Stan
Summary: Recent advances in Markov Chain Monte Carlo (MCMC) simulation have led to the development of a high-level probability modeling language called Stan. In this presentation, I will give a gentle introduction to Bayesian inference using Stan.
Bio: Sean Raleigh received his Ph.D. in mathematics from U.C. San Diego, specializing in geometric topology and knot theory. He is a professor of mathematics at Westminster College and currently chairs the data science program. As part of Sean's professional work, he advocates for Bayesian methods in data analysis and co-directs QUARC, the Quantitative Analysis and Research Cooperative.
Thanks so much @VectorPosse! I updated the meetup event with these details.
Hello there @VectorPosse! Are you all set to present next Wednesday at 12:30pm at Neumont? Looks like we have 30+ folks who have RSVP'd so far.
Yep. All set and looking forward to it.
On Thu, Nov 8, 2018 at 4:50 PM Julia Silge notifications@github.com wrote:
Hello there @VectorPosse https://github.com/VectorPosse! Are you all set to present next Wednesday at 12:30pm at Neumont? Looks like we have 30+ folks who have RSVP'd so far.
— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/slc-rug/future-meetups/issues/9#issuecomment-437199078, or mute the thread https://github.com/notifications/unsubscribe-auth/AHbNnxDy3FqYu4AkBVmPNy7zN7ihT8hvks5utMNXgaJpZM4U4Va_ .
The location is confirmed... this time we'll be upstairs at Neumont.
@VectorPosse Thank you so much for presenting! Would you be willing to share your slides on this github page?
Nevermind... I see the link to it on there!
My pleasure. Thanks for having me.
On Sat, Nov 17, 2018 at 5:53 PM nielsenmarkus11 notifications@github.com wrote:
@VectorPosse https://github.com/VectorPosse Thank you so much for presenting! Would you be willing to share your slides on this github page?
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I could present sometime on using the Stan modeling language. There are various topics one could cover depending on background/interest. From the theoretical side, there is the Hamiltonian MCMC stuff that happens under the hood. On the practical side, there are applications that can be coded up in Stan using the RStan interface. It's also a good vehicle for explaining Bayesian hierarchical modeling.