Bayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data

ArXi:2603.27142v1 Announce Type: cross Time-series analysis is often affected by missing data, a common problem across several fields, including healthcare and environmental monitoring. Multiple Imputation by Chained Equations (MICE) has been prominent for imputing missing values through "fully conditional specification". We extend MICE using the Bayesian framework (Bayes-MICE), utilising Bayesian inference to impute missing values via Marko Chain Monte Carlo (MCMC) sampling to account for uncertainty in MICE model parameters and imputed values.