0000000001322820
AUTHOR
David Dunson
Scalable multiscale density estimation
Although Bayesian density estimation using discrete mixtures has good performance in modest dimensions, there is a lack of statistical and computational scalability to high-dimensional multivariate cases. To combat the curse of dimensionality, it is necessary to assume the data are concentrated near a lower-dimensional subspace. However, Bayesian methods for learning this subspace along with the density of the data scale poorly computationally. To solve this problem, we propose an empirical Bayes approach, which estimates a multiscale dictionary using geometric multiresolution analysis in a first stage. We use this dictionary within a multiscale mixture model, which allows uncertainty in co…
Data from: Wood-inhabiting fungi with tight associations with other species have declined as a response to forest management
Research on mutualistic and antagonistic networks, such as plant–pollinator and host–parasite networks, has shown that species interactions can influence and be influenced by the responses of species to environmental perturbations. Here we examine whether results obtained for directly observable networks generalize to more complex networks in which species interactions cannot be observed directly. As a case study, we consider data on the occurrences of 98 wood-inhabiting fungal species in managed and natural forests. We specifically ask if and how much the positions of wood-inhabiting fungal species within the interaction networks influence their responses to forest management. For this, we…