CBMS 2020

CBMS Regional Conference in the Mathematical Sciences

Bayesian Forecasting and Dynamic Models

University of California Santa Cruz, Tentatively scheduled for August 2-6, 2021

Adequate modeling and forecasting of temporal data, particularly in large-dimensional settings, is key in a wide range of applications.  This area has defined a major research arena in the mathematical and statistical sciences for years, and has also led to intense research activity in methodological, computational and applied areas where these methods are used. In particular, recent important research advances in this area have led to a massive body of literature that comprise new sophisticated models and methods for analysis and forecasting of time series data, as well as powerful computational tools and related software for inference and forecasting in an efficient manner.  Exploring, understanding, and applying these models and tools can be a daunting task for newcomers, imposing a steep barrier into the field.  This conference, along with the monograph derived from it will facilitate introduction to the area by providing a comprehensive review of Bayesian modeling and forecasting tools.

The conference will feature three principal lecturers that will deliver 10 main lectures

Main Lecturer:

Mike West (Duke University) 


Invited Lecturers:

Hedibert Lopes (Insper Brazil)



Raquel Prado (UC Santa Cruz)


Local Organizers: Juhee Lee and Raquel Prado

The conference will also feature a case-study session in a specific area of application to expose junior participants to the process of developing focused statististical tools for highly structured time series data. In addition, the conference will offer ``hands-on'' sessions on practical data analysis and a panel session with industry experts from companies in Northern California. This will provide participants additional exposure on how Bayesian forecasting and dynamic models are applied in practical non-academic settings.  Established and junior researchers, postdoctoral fellows and students will have the opportunity to learn and discuss the major foundational ideas, as well as recent and modern models and computing methods in the area of Bayesian time series and dynamic modeling. The conference aims to attract new researchers to this field. Furthermore, given the regional emphasis of the conference, it is expected that the event will provide an important opportunity for strengthening links and collaborations between multiple groups in the Western United States.