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Causal influence, causal effects, and path analysis in the presence of intermediate confounding
Recent approaches to causal inference have focused on the identification and estimation of \textit{causal effects}, defined as (properties of) the distribution of counterfactual outcomes under hypothetical actions that alter the nodes of a graphical model.
Methodology
Evaluating Forecasts with scoringutils in R
Evaluating forecasts is essential in order to understand and improve forecasting and make forecasts useful to decision-makers.
Methodology Applications Computation
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Statistical models can involve implicitly defined quantities, such as solutions to nonlinear ordinary differential equations (ODEs), that unavoidably need to be numerically approximated in order to evaluate the model.
Computation
BayesMix: Bayesian Mixture Models in C++
We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models.
Computation Other Statistics
High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages
Estimating forest aboveground biomass at fine spatial scales has become increasingly important for greenhouse gas estimation, monitoring, and verification efforts to mitigate climate change.
Applications
