701 research outputs found

    Bayesian modeling via discrete nonparametric priors

    Get PDF
    The availability of complex-structured data has sparked new research directions in statistics and machine learning. Bayesian nonparametrics is at the forefront of this trend thanks to two crucial features: its coherent probabilistic framework, which naturally leads to principled prediction and uncertainty quantification, and its infinite-dimensionality, which exempts from parametric restrictions and ensures full modeling flexibility. In this paper, we provide a concise overview of Bayesian nonparametrics starting from its foundations and the Dirichlet process, the most popular nonparametric prior. We describe the use of the Dirichlet process in species discovery, density estimation, and clustering problems. Among the many generalizations of the Dirichlet process proposed in the literature, we single out the Pitman–Yor process, and compare it to the Dirichlet process. Their different features are showcased with real-data illustrations. Finally, we consider more complex data structures, which require dependent versions of these models. One of the most effective strategies to achieve this goal is represented by hierarchical constructions. We highlight the role of the dependence structure in the borrowing of information and illustrate its effectiveness on unbalanced datasets

    Bayesian modeling via discrete nonparametric priors

    Get PDF
    The availability of complex-structured data has sparked new research directions in statistics and machine learning. Bayesian nonparametrics is at the forefront of this trend thanks to two crucial features: its coherent probabilistic framework, which naturally leads to principled prediction and uncertainty quantification, and its infinite-dimensionality, which exempts from parametric restrictions and ensures full modeling flexibility. In this paper, we provide a concise overview of Bayesian nonparametrics starting from its foundations and the Dirichlet process, the most popular nonparametric prior. We describe the use of the Dirichlet process in species discovery, density estimation, and clustering problems. Among the many generalizations of the Dirichlet process proposed in the literature, we single out the Pitman–Yor process, and compare it to the Dirichlet process. Their different features are showcased with real-data illustrations. Finally, we consider more complex data structures, which require dependent versions of these models. One of the most effective strategies to achieve this goal is represented by hierarchical constructions. We highlight the role of the dependence structure in the borrowing of information and illustrate its effectiveness on unbalanced datasets

    Posterior asymptotics for boosted Hierarchical Dirichlet Process mixtures

    Get PDF
    Bayesian hierarchical models are powerful tools for learning common latent features across multiple data sources. The Hierarchical Dirichlet Process (HDP) is invoked when the number of latent components is a priori unknown. While there is a rich literature on finite sample properties and performance of hierarchical processes, the analysis of their frequentist posterior asymptotic properties is still at an early stage. Here we establish theoretical guarantees for recovering the true data generating process when the data are modeled as mixtures over the HDP or a generalization of the HDP, which we term boosted because of the faster growth in the number of discovered latent features. By extending Schwartz's theory to partially exchangeable sequences we show that posterior contraction rates are crucially affected by the relationship between the sample sizes corresponding to the different groups. The effect varies according to the smoothness level of the true data distributions. In the supersmooth case, when the generating densities are Gaussian mixtures, we recover the parametric rate up to a logarithmic factor, provided that the sample sizes are related in a polynomial fashion. Under ordinary smoothness assumptions more caution is needed as a polynomial deviation in the sample sizes could drastically deteriorate the convergence to the truth

    Crystal structures of Leptospira interrogans FAD-containing ferredoxin-NADP+ reductase and its complex with NADP+

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Ferredoxin-NADP(H) reductases (FNRs) are flavoenzymes that catalyze the electron transfer between NADP(H) and the proteins ferredoxin or flavodoxin. A number of structural features distinguish plant and bacterial FNRs, one of which is the mode of the cofactor FAD binding. <it>Leptospira interrogans </it>is a spirochaete parasitic bacterium capable of infecting humans and mammals in general. <it>Leptospira interrogans </it>FNR (LepFNR) displays low sequence identity with plant (34% with <it>Zea mays</it>) and bacterial (31% with <it>Escherichia coli</it>) FNRs. However, LepFNR contains all consensus sequences that define the plastidic class FNRs.</p> <p>Results</p> <p>The crystal structures of the FAD-containing LepFNR and the complex of the enzyme with NADP<sup>+</sup>, were solved and compared to known FNRs. The comparison reveals significant structural similarities of the enzyme with the plastidic type FNRs and differences with the bacterial enzymes. Our small angle X-ray scattering experiments show that LepFNR is a monomeric enzyme. Moreover, our biochemical data demonstrate that the LepFNR has an enzymatic activity similar to those reported for the plastidic enzymes and that is significantly different from bacterial flavoenzymes, which display lower turnover rates.</p> <p>Conclusion</p> <p>LepFNR is the first plastidic type FNR found in bacteria and, despite of its low sequence similarity with plastidic FNRs still displays high catalytic turnover rates. The typical structural and biochemical characteristics of plant FNRs unveiled for LepFNR support a notion of a putative lateral gene transfer which presumably offers <it>Leptospira interrogans </it>evolutionary advantages. The wealth of structural information about LepFNR provides a molecular basis for advanced drugs developments against leptospirosis.</p

    Practical Recommendations for Optimal Thromboprophylaxis in Patients with COVID-19: A Consensus Statement Based on Available Clinical Trials.

    Get PDF
    Coronavirus disease 2019 (COVID-19) has been shown to be strongly associated with increased risk for venous thromboembolism events (VTE) mainly in the inpatient but also in the outpatient setting. Pharmacologic thromboprophylaxis has been shown to offer significant benefits in terms of reducing not only VTE events but also mortality, especially in acutely ill patients with COVID-19. Although the main source of evidence is derived from observational studies with several limitations, thromboprophylaxis is currently recommended for all hospitalized patients with acceptable bleeding risk by all national and international guidelines. Recently, high quality data from randomized controlled trials (RCTs) further support the role of thromboprophylaxis and provide insights into the optimal thromboprophylaxis strategy. The aim of this statement is to systematically review all the available evidence derived from RCTs regarding thromboprophylaxis strategies in patients with COVID-19 in different settings (either inpatient or outpatient) and provide evidence-based guidance to practical questions in everyday clinical practice. Clinical questions accompanied by practical recommendations are provided based on data derived from 20 RCTs that were identified and included in the present study. Overall, the main conclusions are: (i) thromboprophylaxis should be administered in all hospitalized patients with COVID-19, (ii) an optimal dose of inpatient thromboprophylaxis is dependent upon the severity of COVID-19, (iii) thromboprophylaxis should be administered on an individualized basis in post-discharge patients with COVID-19 with high thrombotic risk, and (iv) thromboprophylaxis should not be routinely administered in outpatients. Changes regarding the dominant SARS-CoV-2 variants, the wide immunization status (increasing rates of vaccination and reinfections), and the availability of antiviral therapies and monoclonal antibodies might affect the characteristics of patients with COVID-19; thus, future studies will inform us about the thrombotic risk and the optimal therapeutic strategies for these patients

    Planck 2013 results. I. Overview of products and scientific results

    Get PDF

    Addition of elotuzumab to lenalidomide and dexamethasone for patients with newly diagnosed, transplantation ineligible multiple myeloma (ELOQUENT-1): an open-label, multicentre, randomised, phase 3 trial

    Get PDF

    Transport distances on random vectors of measures : recent advances in Bayesian nonparametrics

    No full text
    Random vectors of measures are at the core of many recent developments in Bayesian nonparametrics. For a deep understanding of these infinite-dimensional discrete random structures and their impact on the inferential and theoretical properties of the induced models, we consider a class of transport distances based on the Wasserstein distance. The geometrical definition makes it ideal for measuring similarity between distributions with possibly different supports. Moreover, when applied to random vectors of measures with independent increments (completely random vectors), the interesting theoretical properties are coupled with analytical tractability. This leads to a new measure of dependence for completely random vectors and the quantification of the impact of hyperparameters in notable models for exchangeable time-to-event data
    corecore