3 accepted papers
Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior. %, which serves to counteract overfitting. In the context of density estimation, the standard nonparametric Bayesian approach is to target the posterior predic…
Bayesian nonparametric methods are a popular choice for analysing survival data due to their ability to flexibly model the distribution of survival times. These methods typically employ a nonparametric prior on the survival function that is conjugate with respect to right-censored data. Eliciting th…
Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimension grows, and propensity score matching may match highly…