In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is asses
sed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.
The article addresses the problem of detecting presence and location of a small low emission source inside of an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The goal is to re
ach the signal-to-noise ratio (SNR) levels on the order of $10^{-3}$. A Bayesian approach to this problem is implemented in 2D. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov Chain Monte Carlo (MCMC) simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source.
In this paper we explore the relationship between protostellar outflows and turbulence in molecular clouds. Using 3-D numerical simulations we focus on the hydrodynamics of multiple outflows interacting within a parsec scale volume. We explore the ex
tent to which transient outflows injecting directed energy and momentum into a sub-volume of a molecular cloud can be converted into random turbulent motions. We show that turbulence can readily be sustained by these interactions and show that it is possible to broadly characterize an effective driving scale of the outflows. We compare the velocity spectrum obtained in our studies to that of isotropically forced hydrodynamic turbulence finding that in outflow driven turbulence a power law is indeed achieved. However we find a steeper spectrum (beta ~ 3) is obtained in outflow driven turbulence models than in isotropically forced simulations (beta ~ 2). We discuss possible physical mechanisms responsible for these results as well and their implications for turbulence in molecular clouds where outflows will act in concert with other processes such as gravitational collapse.
