Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in Physical Sciences and Mathematics
Extending The Latent Multinomial Model With Complex Error Processes And Dynamic Markov Bases, Simon J. Bonner, Matthew R. Schofield, Patrik Noren, Steven J. Price
Extending The Latent Multinomial Model With Complex Error Processes And Dynamic Markov Bases, Simon J. Bonner, Matthew R. Schofield, Patrik Noren, Steven J. Price
Forestry and Natural Resources Faculty Publications
The latent multinomial model (LMM) of Link et al. [Biometrics 66 (2010) 178–185] provides a framework for modelling mark-recapture data with potential identification errors. Key is a Markov chain Monte Carlo (MCMC) scheme for sampling configurations of the latent counts of the true capture histories that could have generated the observed data. Assuming a linear map between the observed and latent counts, the MCMC algorithm uses vectors from a basis of the kernel to move between configurations of the latent data. Schofield and Bonner [Biometrics 71 (2015) 1070–1080] shows that this is sufficient for some models within the …