Search Results for author:
Found 9 papers, 4 papers with code
Understanding Social Immunity in Ants: A Markovian Approach to Collective Cleaning Strategies
We investigate how worker interactions and previous behaviors influence the evolution of social immunity strategies in response to the presence of pathogens and vulnerable colony members.
Forecasting insect abundance using time series embedding and machine learning
We pre-processed the data using our newly proposed approach and more straightforward approaches commonly used to train machine learning algorithms in time series problems.
Bayesian Additive Main Effects and Multiplicative Interaction Models using Tensor Regression for Multi-environmental Trials
We propose a Bayesian tensor regression model to accommodate the effect of multiple factors on phenotype prediction.
Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package
It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden.
Profiling Television Watching Behaviour Using Bayesian Hierarchical Joint Models for Time-to-Event and Count Data
The model drastically reduces the dimensionality of the data from thousands of observations per customer to 11 customer-level parameter estimates and random effects.
Pattern-Based Prediction of Population Outbreaks
This shows the feasibility of the PBP method in predicting outbreaks in population dynamics.
Variational Inference for Additive Main and Multiplicative Interaction Effects Models
In this article, we consider a variational inference approach for such a model.
Accounting for shared covariates in semi-parametric Bayesian additive regression trees
We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART).
Bayesian Additive Regression Trees with Model Trees
BART assumes regularisation priors on a set of trees that work as weak learners and is very flexible for predicting in the presence of non-linearity and high-order interactions.
