  Measurement error in multinomial data is a well-known and well-studied
inferential problem that is encountered in many fields, including engineering,
biomedical and omics research, ecology, finance, and social sciences.
Surprisingly, methods developed to accommodate measurement error in multinomial
data are typically equipped to handle false negatives or false positives, but
not both. We provide a unified framework for accommodating both forms of
measurement error using a Bayesian hierarchical approach. We demonstrate the
proposed method's performance on simulated data and apply it to acoustic bat
monitoring data.
