David J. Nott

TL;DR: In this paper, Markov chain Monte Carlo (MCMCMC) sampling of the posterior distribution has been used to estimate parameter uncertainty in hydrological models, where prior knowledge about the parameter is combined with information from the available data to produce a probability distribution (the posterior distribution) that describes uncertainty about the parameters and serves as a basis for selecting appropriate values for use in modeling applications.

TL;DR: The accuracy and computational efficiency of the Bayesian version of the synthetic likelihood (BSL) approach is explored in comparison to a competitor known as approximate Bayesian computation (ABC) and its sensitivity to its tuning parameters and assumptions.

TL;DR: In this article, an adaptive Monte Carlo sampling scheme for Bayesian variable selection in linear regression is proposed, which improves on standard Markov chain methods by considering Metropolis-Hastings proposals that make use of accumulated information about the posterior distribution obtained during sampling.

TL;DR: In this paper, the authors proposed the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression, which is adaptive to the signal level by adopting different shrinkage for different coefficients.

TL;DR: Increased transcriptional activity of MYC is a characteristic of basal breast cancers where it mimics a large part of an estrogen response in the absence of the ER, suggesting a mechanism by which these cancers achieve estrogen-independence and providing a potential therapeutic target for this poor prognosis sub group of breast cancer.