Michael Schwob
@michaelschwob.bsky.social
PhD Student at UT Austin | Spatio-Temporal Statistician | Ecological Statistics | Sci-fi and Comedy Fan
Relevant tags: #landscape-genetics #landscape-genomics
#landscape-ecology #ancient-DNA
#spatio-temporal
#dyadic
#Bayesian
#advection-diffusion
#Bronze-Age-Europe
#ecological-statistics
(5/5)
#landscape-ecology #ancient-DNA
#spatio-temporal
#dyadic
#Bayesian
#advection-diffusion
#Bronze-Age-Europe
#ecological-statistics
(5/5)
January 13, 2025 at 5:33 PM
We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. (4/5)
January 13, 2025 at 5:33 PM
We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. (4/5)
... in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. (3/5)
January 13, 2025 at 5:33 PM
... in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. (3/5)
Mechanistic statistical models are commonly used to study the flow of biological processes. In landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence... (2/5)
January 13, 2025 at 5:33 PM
Mechanistic statistical models are commonly used to study the flow of biological processes. In landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence... (2/5)