The BioModelAnalyzer (BMA) is a web based tool for the development of discrete models of biological systems. Through a graphical user interface, it allows rapid development of complex models of gene and protein interaction networks and stability analysis without requiring users to be proficient computer programmers. Whilst stability is a useful specification for testing many systems, testing te...

[1] Predictive uncertainty analysis in hydrologic modeling has become an active area of research, the goal being to generate meaningful error bounds on model predictions. State-space filtering methods, such as the ensemble Kalman filter (EnKF), have shown the most flexibility to integrate all sources of uncertainty. However, predictive uncertainty analyses are typically carried out using a sing...

We present a novel multi-resolution block matching algorithm (BMA) for fast motion estimation. At the coarsest level, a full search BMA (FSBMA) is performed for searching complex or random motion. Concurrently, spatial correlation of motion vector (MV) field is used for searching continuous motion. Here we present an efficient method for searching full resolution MVs without MV decimation even ...

The extraction of the moving foreground from a stationary background from a general video sequence has various applications such as compression of videos and also in the cinematographic effects. For this foreground extraction the main algorithm used is the Block Matching Algorithm (BMA) which is the motion estimation algorithm employed in the MPEG compression standards. The noise is estimated f...

Bayesian Model Averaging (BMA) is well known for improving predictive accuracy by averaging inferences over all models in the model space. However, Markov chain Monte Carlo (MCMC) sampling, as the standard implementation for BMA, encounters difficulties in even relatively simple model spaces. We introduce a minimum message length (MML) coupled MCMC methodology, which not only addresses these di...

We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging (BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper’s BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which lea...

Abstract The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of predictive distributions, extending the utility function to any proper scoring rule, using Pareto smooth...

The topic of my dissertation research is Bayesian methods in the social sciences. There will be several parts to the dissertation. One part, the focus of this proposal, is a novel method called tuned Bayesian model averaging (tBMA) that modifies Bayesian model averaging (BMA) by using the data to inform the prior specification. BMA is a tool that accounts for model uncertainty in typical regres...

Chilling stress is an important constraint for maize seed establishment in the field. In this study, a type of "on-off" thermoresponsive coating agent containing poly (N-isopropylacrylamide-co-butylmethacrylate) (Abbr. P(NIPAm-co-BMA)) hydrogel was developed to improve the chilling tolerance of coated maize seed. The P(NIPAm-co-BMA) hydrogel was synthesized by free-radical polymerization of N-i...