Latent multinomial models for capture-recapture data with latent identification

Wei Zhang (University of Glasgow)

Friday 17th September 15:00-16:00


Latent multinomial models (LMMs) are a class of models in which observed count data arise as a summary of an unobservable multinomial random variable. Bayesian Markov chain Monte Carlo methods have been well developed for fitting these models. However, one obvious limitation is that model fitting using these methods can take a long time, even for moderate sized data sets. In the first part of this talk, I will introduce a fast maximum likelihood estimation approach to fit LMMs, using an approximate likelihood constructed via the saddlepoint approximation. In the second part, I will introduce some recent applications of the LMM for modelling capture-recapture data that are often collected for wildlife surveys. The LMM can be particularly useful when detected individuals are not identified with certainty.

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