5 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
False Positives, False Negatives, and the Detection-Only Problem: A Hierarchical Model for Species Occurrence with Observation Error
Kabiru Abubakari, Eleni Matechou, Marie C. Henniges +3
Monitoring species occurrence is essential for understanding biodiversity change, informing conservation decisions, and assessing the impact of environmental pressures on ecosystem…
A capture-recapture hidden Markov model framework for register-based inference of population size and dynamics
Lucy Y Brown, Eleni Matechou, Bruno Santos +1
Accurate inference on population dynamics, such as migration and changes in population size, is essential for policymaking, resource allocation and demographic research. Traditiona…
Hidden Markov models with an unknown number of states and a repulsive prior on the state parameters
Ioannis Rotous, Alex Diana, Alessio Farcomeni +2
Hidden Markov models (HMMs) offer a robust and efficient framework for analyzing time series data, modelling both the underlying latent state progression over time and the observat…
eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring
Alex Diana, Eleni Matechou, Jim Griffin +5
DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequence…
Fast Bayesian inference for large occupancy data sets, using the Polya-Gamma scheme
Alex Diana, Emily Dennis, Eleni Matechou +1
In recent years, the study of species' occurrence has benefited from the increased availability of large-scale citizen-science data. Whilst abundance data from standardized monitor…
Bayesian analysis of Jolly-Seber type models; incorporating heterogeneity in arrival and departure
E. Matechou, G. Nicholls, B. J. T. Morgan +2
We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We…