Time to Result for Pathogen Identification and Antimicrobial Susceptibility Testing of Bronchoalveolar Lavage and Endotracheal Aspirate Specimens in U.S. Acute Care Hospitals.

Identification (ID) and antimicrobial susceptibility testing (AST) of respiratory pathogens are critical to the management of patients with pneumonia to facilitate optimal antibiotic therapy selection. Few studies have examined the time to results (TTR) for this critical specimen, and such data can be valuable for benchmarking the current paradigm of diagnostic approaches. TTR for bronchoalveolar lavage (BAL) and endotracheal aspirate (ETA) specimens from hospitalized patients was evaluated using the Premier Healthcare Database, a comprehensive database of 194 U.

Diagnostics for insufficiencies of posterior calculations in Bayesian signal inference.

We present an error-diagnostic validation method for posterior distributions in Bayesian signal inference, an advancement of a previous work. It transfers deviations from the correct posterior into characteristic deviations from a uniform distribution of a quantity constructed for this purpose. We show that this method is able to reveal and discriminate several kinds of numerical and approximation errors, as well as their impact on the posterior distribution.

Improving stochastic estimates with inference methods: calculating matrix diagonals.

Estimating the diagonal entries of a matrix, that is not directly accessible but only available as a linear operator in the form of a computer routine, is a common necessity in many computational applications, especially in image reconstruction and statistical inference. Here, methods of statistical inference are used to improve the accuracy or the computational costs of matrix probing methods to estimate matrix diagonals. In particular, the generalized Wiener filter methodology, as developed within information field theory, is shown to significantly improve estimates based on only a few sampling probes, in cases in which some form of continuity of the solution can be assumed.

Reconstructing signals from noisy data with unknown signal and noise covariance.

We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance.