Excursions in the Bayesian treatment of model error.

Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We consider two settings.

CSN5A Subunit of COP9 Signalosome Temporally Buffers Response to Heat in Arabidopsis.

The COP9 (constitutive photomorphogenesis 9) signalosome (CSN) is an evolutionarily conserved protein complex which regulates various growth and developmental processes. However, the role of CSN during environmental stress is largely unknown. Using Arabidopsis as model organism, we used CSN hypomorphic mutants to study the role of the CSN in plant responses to environmental stress and found that heat stress specifically enhanced the growth of csn5a-1 but not the growth of other hypomorphic photomorphogenesis mutants tested.

Applying diffusion-based Markov chain Monte Carlo.

We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC. A variety of motivations for the approach are reviewed in the context of Bayesian analysis.