Bayesian capture-recapture inference with hidden Markov models
VIBASS6 - Invited Course
This course (12 hours) is provided by Dr. Olivier Gimenez (Centre d’Écologie Fonctionnelle & Évolutive, Montpellier, France). He is the author of the open on-line book Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models: Theory and Case Studies in R.
Statisticians and applied researchers with strong interest applications of Hidden Markov Models (HMMs), particularly in ecology. No previous experience with
Nimbleor Bayesian statistics is assumed, but knowledge of
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 two-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
Nimblepackage 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.
It is recommended that people attending are familiar with R (https://www.r-project.org/) and with the basic of the Bayesian approach. It would be beneficial if attendees could bring their laptop with the latest version of R and
Materials for the course are freely available on line at: https://oliviergimenez.github.io/bayesian-cr-workshop/