Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration

Thermodynamic integration (TI) for computing marginal likelihoods is based on an inverse annealing path from the prior to the posterior distribution. In many cases, the resulting estimator suffers from high variability, which particularly stems from the prior regime. When comparing complex models with differences in a comparatively small number of parameters, intrinsic errors from sampling fluctuations may outweigh the differences in the log marginal likelihood estimates. In the present article, we propose a thermodynamic integration scheme that directly targets the log Bayes factor. The method is based on a modified annealing path between the posterior distributions of the two models compared, which systematically avoids the high variance prior regime. We combine this scheme with the concept of non-equilibrium TI to minimise discretisation errors from numerical integration. Results obtained on Bayesian regression models applied to standard benchmark data, and a complex hierarchical model applied to biopathway inference, demonstrate a significant reduction in estimator variance over state-of-the-art TI methods

Network Reconstruction with Realistic Models

We extend a recently proposed gradient-matching method for inferring interactions in complex systems described by differential equations in various respects: improved gradient inference, evaluation of the influence of the prior on kinetic parameters, comparative evaluation of two model selection paradigms: marginal likelihood versus DIC (divergence information criterion), comparative evaluation of different numerical procedures for computing the marginal likelihood, extension of the methodology from protein phosphorylation to transcriptional regulation, based on a realistic simulation of the underlying molecular processes with Markov jump processes

Seismic slip of oceanic strike-slip earthquakes

Oceanic strike-slip earthquakes occur on transform faults and fracture zones that cut across thousands of kilometers of seafloor. The largest of these events often rupture a considerable portion of their associated fault and can provide a comprehensive look at seismic slip across the entire fault plane as well as constraints on the depth extent of seismic slip. It is generally accepted that seismic and aseismic slip along oceanic transform faults is thermally controlled, however composition and geometry have been proposed as significant controls on some faults. High strain rates are a mechanism to achieve greater rupture depths, such as the unusually deep centroids reported for the largest strike-slip earthquake recorded to date, the 2012 MW 8.6 Indian Ocean earthquake. Detailed studies of notable earthquakes and a scattering of well-known faults have been of great use in elucidating oceanic strike-slip rupture. Determining if observed behavior is characteristic of all oceanic strike-slip faults requires a different approach. To resolve how seismic and aseismic slip are controlled with depth and along strike, well-constrained depths of many earthquakes along oceanic strike-slip faults are determined by modeling teleseismic body waves. Finite-fault slip inversions are calculated for the largest, most recent, and best-recorded oceanic strike-slip events. The constrained depth and along-strike location of slip for numerous oceanic earthquakes on strike-slip faults illuminates the distribution of seismic rupture on these faults in detail, as well as in unprecedented breadth through the examination of oceanic faults in a range of spreading rates and lithosphere ages. These well-constrained depths are within the expected limit to brittle failure (600-800ºC) and show that seismic rupture extends throughout the upper mantle to the crust. Observations of seismic rupture along an oceanic strike-slip fault also provide a comparison to the behavior of continental strike-slip faults that pose a far greater hazard to population centers, such as the San Andreas Fault in the Western United States and the North Anatolian Fault in Turkey

The implementation of 360-degree feedback for high school DECA officers

Includes bibliographical references

Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction

In this research, we hypothesized that novel biomechanical parameters are discriminative in patients following acute ST-segment elevation myocardial infarction (STEMI). To identify these biomechanical biomarkers and bring computational biomechanics ‘closer to the clinic’, we applied state-of-the-art multiphysics cardiac modelling combined with advanced machine learning and multivariate statistical inference to a clinical database of myocardial infarction. We obtained data from 11 STEMI patients (ClinicalTrials.gov NCT01717573) and 27 healthy volunteers, and developed personalized mathematical models for the left ventricle (LV) using an immersed boundary method. Subject-specific constitutive parameters were achieved by matching to clinical measurements. We have shown, for the first time, that compared with healthy controls, patients with STEMI exhibited increased LV wall active tension when normalized by systolic blood pressure, which suggests an increased demand on the contractile reserve of remote functional myocardium. The statistical analysis reveals that the required patient-specific contractility, normalized active tension and the systolic myofilament kinematics have the strongest explanatory power for identifying the myocardial function changes post-MI. We further observed a strong correlation between two biomarkers and the changes in LV ejection fraction at six months from baseline (the required contractility (r = − 0.79, p < 0.01) and the systolic myofilament kinematics (r = 0.70, p = 0.02)). The clinical and prognostic significance of these biomechanical parameters merits further scrutinization

Network Reconstruction with Realistic Models

We extend a recently proposed gradient-matching method for inferring interactions in complex systems described by differential equations in various respects: improved gradient inference, evaluation of the influence of the prior on kinetic parameters, comparative evaluation of two model selection paradigms: marginal likelihood versus DIC (divergence information criterion), comparative evaluation of different numerical procedures for computing the marginal likelihood, extension of the methodology from protein phosphorylation to transcriptional regulation, based on a realistic simulation of the underlying molecular processes with Markov jump processes