The 5th Bayesian Young Statisticians Meeting, BAYSM2020, will take place in Kunming, China (June 26-27, 2020) as a satellite to the ISBA 2020 world meeting. BAYSM is the official conference of j-ISBA, the junior section of the International Society for Bayesian Analysis. It is intended for Ph.D. Students, M.S. Students, Post-Docs, Young and Junior researchers working in the field of Bayesian statistics, providing an opportunity to connect with the Bayesian community at large. Senior discussants will be present at each session, providing participants with hints, suggestions and comments to their work. Distinguished professors of the Bayesian community will also participate as keynote speakers, making an altogether exciting program.
Registration is now open (https://baysm2020.uconn.edu/registration) and will be available with an early bird discount until May 1, 2020. The event will be hosted at the Science Hall of Yunnan University (Kunming, China) right before ISBA 2020 world meeting. BAYSM 2020 will include social events, providing the opportunity to get to know other junior Bayesians.
Young researchers interested in giving a talk or presenting a poster are invited to submit an extended abstract by March 29, 2020. All the instructions for the abstract submission are reported at the page https://baysm2020.uconn.edu/call-dates
Thanks to the generous support of ISBA, a number of travel awards are available to support young researchers.
Keynote speakers:
Maria De Iorio
David Dunson
Sylvia Frühwirth-Schnatter
Xuanlong Nguyen
Amy Shi
Jessica Utts
Confirmed discussants:
Jingheng Cai
Li Ma
Fernando Quintana
Francesco Stingo
Anmin Tang
Yemao Xia
While the meeting is organized for and by junior Bayesians, attendance is open to anyone who may be interested. For more information, please visit the conference website: https://baysm2020.uconn.edu/
The Gaussian target associated with this sample stands right in the middle of the two clouds, as identified by Wang et al. And the leapfrog integration path for (ε,L)=(0.15,50)
keeps jumping between the two ridges (or tails) , with no stop in the middle. Changing ever so slightly (ε,L) to (ε,L)=(0.16,40) does not modify the path very much
but the HMC output is quite different since the cloud then sits right on top of the target
and for (ε,L)=(0.16,40)
does not help, except to point at a sequence located far in the tails of this Hamiltonian, surprisingly varying when supposed to be constant. At first, we thought the large value of ε was to blame but much smaller values still return poor convergence performances. As below for (ε,L)=(0.01,450)
