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Advances in Markov chain Monte Carlo methodology and computation

Abstract Number:

1756 

Submission Type:

Topic-Contributed Paper Session 

Participants:

James Flegal (1), James Flegal (1), Vivekananda Roy (2), Rebecca Kurtz-Garcia (3), Stephen Berg (3), Maryclare Griffin (3), James Flegal (1)

Institutions:

(1) University of California-Riverside, N/A, (2) Iowa State University, N/A, (3) N/A, N/A

Chair:

James Flegal  
University of California-Riverside

Session Organizer:

James Flegal  
University of California-Riverside

Speaker(s):

Vivekananda Roy  
Iowa State University
Rebecca Kurtz-Garcia  
N/A
Stephen Berg  
N/A
Maryclare Griffin  
N/A
James Flegal  
University of California-Riverside

Session Description:

The session titled "Advances in Markov Chain Monte Carlo Methodology and Computation" brings together leading researchers in the field of statistics to present and discuss cutting-edge developments in Markov Chain Monte Carlo (MCMC) methodology and computation. The session aims to provide an overview of some recent advancements, addressing both theoretical and practical aspects of MCMC techniques.

The focus of the session is on exploring novel approaches, algorithms, and applications related to MCMC. The participating researchers will delve into various aspects of MCMC methodology, including geometric approaches, kernel bandwidth selection, shape-constrained inference, and structured shrinkage priors. The session aims to foster a deeper understanding of the challenges and opportunities in these areas.

Content:
A Riemannian Geometric Approach to MCMC by Dr. Vivekananda Roy
Bandwidth Selection for Zero Lugsail Kernels by Dr. Rebecca Kurtz-Garcia
Statistical and Computational Aspects of Shape-Constrained Inference for Covariance Function Estimation by Dr. Stephen Berg
Structured Shrinkage Priors by Dr. Maryclare Griffin
Simultaneous Confidence Bands for (Markov Chain) Monte Carlo Simulations by Dr. James Flegal

The session is timely as it addresses the current state of the art in MCMC methodology. Given the increasing relevance of MCMC in various scientific disciplines, the session will provide attendees with up-to-date knowledge on advancements that are shaping the future of statistical computation.

Sponsors:

International Society for Bayesian Analysis (ISBA) 3
Section on Bayesian Statistical Science 1
Section on Statistical Computing 2

Theme: Statistics and Data Science: Informing Policy and Countering Misinformation

Yes

Applied

Yes

Estimated Audience Size

Medium (80-150)

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.

I understand