Focused Bayesian Prediction

We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples we find notable gains in predictive accuracy relative to conventional likelihood-based prediction

Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

Baseline local hemodynamics as predictor of lumen remodeling at 1-year follow-up in stented superficial femoral arteries

In-stent restenosis (ISR) is the major drawback of superficial femoral artery (SFA) stenting. Abnormal hemodynamics after stent implantation seems to promote the development of ISR. Accordingly, this study aims to investigate the impact of local hemodynamics on lumen remodeling in human stented SFA lesions. Ten SFA models were reconstructed at 1-week and 1-year follow-up from computed tomography images. Patient-specific computational fluid dynamics simulations were performed to relate the local hemodynamics at 1-week, expressed in terms of time-averaged wall shear stress (TAWSS), oscillatory shear index and relative residence time, with the lumen remodeling at 1-year, quantified as the change of lumen area between 1-week and 1-year. The TAWSS was negatively associated with the lumen area change (ρ = − 0.75, p = 0.013). The surface area exposed to low TAWSS was positively correlated with the lumen area change (ρ = 0.69, p = 0.026). No significant correlations were present between the other hemodynamic descriptors and lumen area change. The low TAWSS was the best predictive marker of lumen remodeling (positive predictive value of 44.8%). Moreover, stent length and overlapping were predictor of ISR at follow-up. Despite the limited number of analyzed lesions, the overall findings suggest an association between abnormal patterns of WSS after stenting and lumen remodeling

Muonium as a hydrogen analogue in silicon and germanium; quantum effects and hyperfine parameters

We report a first-principles theoretical study of hyperfine interactions, zero-point effects and defect energetics of muonium and hydrogen impurities in silicon and germanium. The spin-polarized density functional method is used, with the crystalline orbitals expanded in all-electron Gaussian basis sets. The behaviour of hydrogen and muonium impurities at both the tetrahedral and bond-centred sites is investigated within a supercell approximation. To describe the zero-point motion of the impurities, a double adiabatic approximation is employed in which the electron, muon/proton and host lattice degrees of freedom are decoupled. Within this approximation the relaxation of the atoms of the host lattice may differ for the muon and proton, although in practice the difference is found to be slight. With the inclusion of zero-point motion the tetrahedral site is energetically preferred over the bond-centred site in both silicon and germanium. The hyperfine and superhyperfine parameters, calculated as averages over the motion of the muon, agree reasonably well with the available data from muon spin resonance experiments.Comment: 20 pages, including 9 figures. To appear in Phys. Rev.

Beyond Sentinel Lymph Node: Outcomes of Indocyanine Green-Guided Pelvic Lymphadenectomy in Endometrial and Cervical Cancer

Background: The aim of our study was to compare the number of lymph nodes removed during indocyanine green (ICG)-guided laparoscopic/robotic pelvic lymphadenectomy with standard systematic lymphadenectomy in endometrial cancer (EC) and cervical cancer (CC). Methods: This is a multicenter retrospective comparative study (Clinical Trial ID: NCT04246580; updated on 31 January 2023). Women affected by EC and CC who underwent laparoscopic/robotic systematic pelvic lymphadenectomy, with (cases) or without (controls) the use of ICG tracer injection within the uterine cervix, were included in the study. Results: The two groups were homogeneous for age (p = 0.08), Body Mass Index, International Federation of Gynaecology and Obstetrics (FIGO) stages (p = 0.41 for EC; p = 0.17 for CC), median estimated blood loss (p = 0.76), median operative time (p = 0.59), and perioperative complications (p = 0.66). Nevertheless, the number of lymph nodes retrieved during surgery was significantly higher (p = 0.005) in the ICG group (n = 18) compared with controls (n = 16). Conclusions: The accurate and precise dissection achieved with the use of the ICG-guided procedure was associated with a higher number of lymph nodes removed in the case of systematic pelvic lymphadenectomy for EC and CC

Seasonal cycle of precipitation variability in South America on intraseasonal timescales

The seasonal cycle of the intraseasonal (IS) variability of precipitation in South America is described through the analysis of bandpass filtered outgoing longwave radiation (OLR) anomalies. The analysis is discriminated between short (10--30 days) and long (30--90 days) intraseasonal timescales. The seasonal cycle of the 30--90-day IS variability can be well described by the activity of first leading pattern (EOF1) computed separately for the wet season (October--April) and the dry season (May--September). In agreement with previous works, the EOF1 spatial distribution during the wet season is that of a dipole with centers of actions in the South Atlantic Convergence Zone (SACZ) and southeastern South America (SESA), while during the dry season, only the last center is discernible. In both seasons, the pattern is highly influenced by the activity of the Madden--Julian Oscillation (MJO). Moreover, EOF1 is related with a tropical zonal-wavenumber-1 structure superposed with coherent wave trains extended along the South Pacific during the wet season, while during the dry season the wavenumber-1 structure is not observed. The 10--30-day IS variability of OLR in South America can be well represented by the activity of the EOF1 computed through considering all seasons together, a dipole but with the stronger center located over SESA. While the convection activity at the tropical band does not seem to influence its activity, there are evidences that the atmospheric variability at subtropical-extratropical regions might have a role. Subpolar wavetrains are observed in the Pacific throughout the year and less intense during DJF, while a path of wave energy dispersion along a subtropical wavetrain also characterizes the other seasons. Further work is needed to identify the sources of the 10--30-day-IS variability in South America

An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area which we feel deserves much further attention. Towards this aim, this paper proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance is studied in several applications. Through a Bayesian variable selection example, the authors demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a trimodal density and a Bayesian mixture modeling application. Lastly, through a 2D Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as appendices