O'Bayes 2022: Objective Bayes Methodology Conference
Department of Statistics, UC Santa Cruz

Accepted Posters

  1. Kernel Density Bayesian Inverse Reinforcement Learning
    Aishwarya Mandyam Diana Cai, Didong Li, Andrew Jones, Barbara E. Engelhardt
  2. A Bayesian Method for Biclustering Multivariate Ordinal Data with Informative Censoring
    Alice Giampino, Bernardo Nipoti and Antonio Canale
  3. A comparison of objective priors for Gaussian conditional autoregressive models.
    Amri Alaa
  4. A Bayesian statistical analysis of Puerto Rico’s demographic trends: What can we expect in the coming years?
    Angelica Rosario and Luis Pericchi
  5. A universal robust bound for Bayes Factors.
    Richard Clare and Luis Pericchi
  6. Hellinger information matrix in parametric estimation and objective priors.
    Arkady Shemyakin
  7. Multiscale Analysis of Bayesian Cox Piecewise Constant Hazards Model
    Bo Y.-C. Ning and Ismael Castillo
  8. A Formal Bayesian Approach to SENSE Image Reconstruction Leads to More Statistically Significant Task Activation in fMRI
    Chase Sakitis, Andrew Brown and Daniel Rowe
  9. An Approach to Analyzing the Household Pulse Survey using Bayesian Spatio-Temporal Unit-Level Models Under Informative Sampling
    Daniel Vedensky, Alex Sun, Paul Parker and Scott Holan
  10. Quantifying Observed Prior Impact
    David E Jones, Robert N Trangucci, Yang Chen
  11. Finite mixture models do not reliably learn the number of components: an investigation of posteriors, power posteriors, and information criteria
    Diana Cai, Trevor Campbell and Tamara Broderick
  12. Inference of random graphs with a surrogate likelihood function
    Dingbo Wu(c.a.) and Fangzheng Xie
  13. Sampling-free Bayesian Model Calibration
    Erika McPhillips and Mengyang Gu
  14. Thinned random measures for sparse graphs with overlapping communities
    Federica Zoe Ricci, Erik Sudderth and Michele Guindani
  15. Scalable model selection with mixtures of g-priors in large data settings
    Jacob Fontana and Bruno Sansó
  16. Structured Mixture of Continuation-ratio Logits Models for Ordinal Regression
    Jizhou Kang and Athanasios Kottas
  17. Foundations of Objective Probability: Extending the Finite to the Infinite
    Kevin S. Van Horn
  18. Reference Priors for the Generalized Extreme Value Distribution
    Likun Zhang
  19. Bayesian analysis of GLMMs with nonlocal priors for genome-wide association studies
    Marco Ferreira, Shuangshuang Xu and Jacob Williams
  20. How good is your Bayesian CLT? Finite-sample error bounds for a variety of useful divergences
    Mikolaj Kasprzak, Ryan Giordano and Tamara Broderick
  21. Partial Membership Models for Functional Data
    Nicholas Marco, Damla Senturk and Donatello Telesca
  22. Bayesian Semi-Parametric Inference for Binary Regression
    Paolo Onoratti and Brunero Liseo
  23. Bayesian non-parametric inference for multivariate peaks-over-threshold models
    Peter Trubey and Bruno Sansó
  24. Uncertainty Quantification in Assessing Storm Surge
    Pulong Ma, Taylor Asher, Gabriel Toro and Andrew Cox
  25. Using Bayesian spatio-temporal models to optimize resource allocation for the opioid epidemic in North Carolina
    Qianyu Dong and Staci Hepler
  26. Sparse probabilistic Canonical Correlation Analysis
    Quinn Simonis and Martin Wells
  27. The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
    Raj Agrawal
  28. Hierarchical Bayesian Models For Spatially Correlated Areal Non-Gaussian Multitype Survey Data Using Covariates That Are Measured With Error
    Saikat Nandy, Scott Holan, Jonathan Bradley and Christopher Wikle
  29. Bayesian Modeling of Interaction between Features in Sparse Multivariate Count Data with Application to Microbiome Study
    Shuangjie Zhang, Yuning Shen, Irene A. Chen, and Juhee Lee
  30. Bayesian Joint Modeling and Selection among Many Biomarkers Measured Longitudinally
    Soumya Sahu, Sanjib Basu, Jiehuan Sun and Joelle Hallak
  31. Consistent Bayesian Variable Selection in High-Dimensional Hierarchical Regression
    Srijata Samanta
  32. A Bayesian Multiscale Spatial Model with Proximity Constraints
    Sudipto Saha
  33. Bayesian Semi-supervised Classification for Data Integration in ERP-based Brain-Computer Interface
    Tianwen Ma, Jane Huggins and Jian Kang
  34. Bi-directional clustering via averaged mixture of finite mixtures
    Tianyu Pan, Weining Shen and Guanyu Hu
  35. A Bayesian Modified Ising Model for Identifying Spatially Variable Genes from Spatial Transcriptomics Data
    Xi Jiang, Qiwei Li and Guanghua Xiao
  36. Faithful emulation of expensive computer simulations with high-dimensional input space through active learning
    Xinyi Fang and Mengyang Gu
  37. A Bayesian hierarchical model for COVID-19 related Cause-of-death Assignment using Verbal Autopsies
    Yu Zhu and Zehang Li
  38. Modeling Neural Population Coordination via a Block Correlation Matrix
    Yunran Chen and Surya Tokdar
  39. Bayesian Nonparametric Erlang Mixture Modeling for Survival Analysis
    Yunzhe Li, Juhee Lee and Athanasios Kottas
  40. Bayesian Selection and Smoothing for GAM
    Zihe (Zach) Liu and Feng Liang