Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models in NIMBLE

Sunday, 26 June 2022 (Half day)

Presenters: Olivier Gimenez, CNRS, France; Daniel Turek, Williams College, USA

The hidden Markov modelling (HMM) framework has gained much attention in the ecological literature over the last decade, and has been suggested as a general modelling framework for the demography of plant and animal populations. In particular, HMMs are increasingly used to analyse capture-recapture data and estimate key population parameters (e.g., survival, dispersal, recruitment or abundance) with applications in all fields of ecology. In parallel, Bayesian statistics is relatively well established in ecology and related disciplines, because it resonates with scientific reasoning and allows accommodating uncertainty smoothly. The popularity of Bayesian statistics also comes from the availability of free pieces of software that allow practitioners to code their own analyses.

In this 1-day workshop, we offer a Bayesian treatment of HMMs applied to capture-recapture data. Through a combination of lectures, real case studies and live demonstrations, you will get acquainted with multi-site, multi-state and multi-event capture-recapture models.

We will use the R Nimble package that is seen by many as the future of ecological data modelling because it i) helps overcome computational limitations that ecologists are faced with when dealing with complex models and/or big data, and iii) provides samplers that can deal with discrete latent states that are typical of capture-recapture data analysis.

All material available at

FORMAT: On-line only
Cost: R650 per person (US$45)