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Contributed Papers

Bayesian Inference in Categorical, Count and Privacy-preserving Latent Variable Models

Thu, Aug 7, 10:30 AM - 12:20 PM Room CC-202C Music City Center

Mark LittleChair

Section on Bayesian Statistical Science

4 Presentations

A Bayesian approach for the estimation of the mitigated�fraction for ordinal response

10:35 AM - 10:50 AM

Co-authors: Steephanson Anthonymuthu (The Ohio State University), Sanjeewani Weerasingha (The Ohio State University), Steephanson Anthonymuthu (The Ohio State University)

MCMC computation Ordinal data link function Bayesian approach GLMM

PRECISE: PRivacy-loss-Efficient and Consistent Inference based on poSterior quantilEs

10:50 AM - 11:05 AM

Co-authors: RUYU ZHOU, Fang Liu (University of Notre Dame), RUYU ZHOU

Bayesian differential privacy privacy-preserving inference MSE consistency privacy loss quantiles

Bayesian Estimation for Negative Binomial INGARCH Models with Limited Prior Information

11:05 AM - 11:20 AM

Co-authors: Xiaoyin Wang (Towson University), Yunwei Cui (Towson University), Xiaoyin Wang (Towson University)

NB-INGARCH Bayesian Estimation Minimally informative priors Hierarchical priors Markov Chain Monte Carlo Over-dispersion

Bayesian method for Analyzing Nominal Measures with Missing Values Using the Multinomial Probit Models.

11:20 AM - 11:50 AM

Co-authors: Suwash Silwal (Michigan Technological university), Xiao Zhang (Michigan Technological University), Suwash Silwal (Michigan Technological university)

missing data categorical data MCMC multinomial probit

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