• Publications
  • Influence
Bayesian Data Analysis
TLDR
Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided. Expand
Understanding predictive information criteria for Bayesian models
TLDR
The Akaike, deviance, and Watanabe-Akaike information criteria are reviewed from a Bayesian perspective and it is better understood, through small examples, how these methods can apply in practice. Expand
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
TLDR
An efficient computation of LOO is introduced using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights, and it is demonstrated that PSIS-LOO is more robust in the finite case with weak priors or influential observations. Expand
Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.
TLDR
Although the modified NIH classification is the best criteria to identify a single high-risk group for consideration of adjuvant therapy, the prognostic contour maps resulting from non-linear modelling are appropriate for estimation of individualised outcomes. Expand
One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: a randomized trial.
TLDR
Adjuvant imatinib administered for 12 months after surgery has improved recurrence-free survival (RFS) of patients with operable gastrointestinal stromal tumor (GIST) compared with placebo and overall survival of GIST patients with a high risk of Gist recurrence. Expand
Rao-Blackwellized particle filter for multiple target tracking
TLDR
A new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes is proposed. Expand
Gaussian processes with monotonicity information
TLDR
Behaviour of the proposed method is illustrated with artificial regression examples, and the method is used in a real world health care classification problem to include monotonicity information with respect to one of the covariates. Expand
A survey of Bayesian predictive methods for model assessment, selection and comparison
TLDR
A unified review of Bayesian predictive model assessment and selection methods, and of methods closely related to them, with an emphasis on how each method approximates the expected utility of using a Bayesian model for the purpose of predicting future data. Expand
Sparsity information and regularization in the horseshoe and other shrinkage priors
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but has previously suffered from two problems. First, there has been no systematic way of specifying aExpand
...
1
2
3
4
5
...