3期

最新文章

• GRAPH LINK PREDICTION IN COMPUTER NETWORKS USING POISSON MATRIX FACTORISATION AI翻译
  作者：Passino, Francesco Sanna; Turcotte, Melissa J. M.; Heard, Nicholas A.
  作者单位：Imperial College London; Microsoft
  摘要：Graph link prediction is an important task in cybersecurity: relationships between entities within a computer network, such as users interacting with computers or system libraries and the corresponding processes that use them, can provide key insights into adversary behaviour. Poisson matrix factorisation (PMF) is a popular model for link prediction in large networks, particularly useful for its scalability. In this article PMF is extended to include scenarios that are commonly encountered in ...
• ASYMMETRIC TAIL DEPENDENCE MODELING, WITH APPLICATION TO CRYPTOCURRENCY MARKET DATA AI翻译
  作者：Gong, Yan; Huser, Raphael
  作者单位：King Abdullah University of Science & Technology
  摘要：Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce, but the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stability and associated risks. For investors it is crucial to uncover the dependence relationships between cryptocurrencies for a more resilient portfolio diversification. Moreover, the stochastic behavior in both tails is important, as long positions are sensitive to a decrease i...
• ANALYSIS OF PRESENCE-ONLY DATA VIA EXACT BAYES, WITH MODEL AND EFFECTS IDENTIFICATION AI翻译
  作者：Moreira, Guido A.; Gamerman, Dani
  作者单位：Universidade do Minho; Universidade Federal do Rio de Janeiro
  摘要：This paper provides an exact modeling approach for the analysis of presence-only ecological data. Our proposal is also based on frequently used inhomogeneous Poisson processes but does not rely on model approximations, unlike other approaches. Exactness is achieved via a data augmentation scheme. One of the augmented processes can be interpreted as the unobserved occurrences of the relevant species, and its posterior distribution can be used to make predictions of the species over the region o...
• CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH AI翻译
  作者：Warren, Joshua L.; Chang, Howard H.; Warren, Lauren K.; Strickland, Matthew J.; Darrow, Lyndsey A.; Mulholland, James A.
  作者单位：Yale University; Emory University; Research Triangle Institute; Nevada System of Higher Education (NSHE); University of Nevada Reno; University System of Georgia; Georgia Institute of Technology
  摘要：Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchi...
• HETEROGENEOUS CAUSAL EFFECTS WITH IMPERFECT COMPLIANCE: A BAYESIAN MACHINE LEARNING APPROACH AI翻译
  作者：Bargagli-Stoffi, Falco J.; de Witte, Kristof; Gnecco, Giorgio
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; KU Leuven; IMT School for Advanced Studies Lucca
  摘要：This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrumental Variable (BCF-IV) methodology outperforms other machine learning techniques tailored for causal inference in discovering and estimating the heterogeneous causal effects whi...
• BAYESIAN SEMIPARAMETRIC LONG MEMORY MODELS FOR DISCRETIZED EVENT DATA AI翻译
  作者：Chakraborty, Antik; Ovaskainen, Otso; Dunson, David B.
  作者单位：Purdue University System; Purdue University; Duke University; University of Jyvaskyla; University of Helsinki; Norwegian University of Science & Technology (NTNU)
  摘要：We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent ...
• STRUCTURED HIERARCHICAL MODELS FOR PROBABILISTIC INFERENCE FROM PERTURBATION SCREENING DATA AI翻译
  作者：Dirmeier, Simon; Beerenwinkel, Niko
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Genetic perturbation screening is an experimental method in biology to study cause and effect relationships between different biological entities. However, knocking out or knocking down genes is a highly error-prone process that complicates estimation of the effect sizes of the interventions. Here, we introduce a family of generative models, called the structured hierarchical model (SHM) for probabilistic inference of causal effects from perturbation screens. SHMs utilize classical hierarchica...
• BAYESIAN LOCAL FALSE DISCOVERY RATE FOR SPARSE COUNT DATA WITH APPLICATION TO THE DISCOVERY OF HOTSPOTS IN PROTEIN DOMAINS AI翻译
  作者：Gauran, Iris Ivy M.; Park, Junyong; Rattsev, Ilia; Peterson, Thomas A.; Kann, Maricel G.; Park, DoHwan
  作者单位：King Abdullah University of Science & Technology; Seoul National University (SNU); University System of Maryland; University of Maryland College Park; University of California System; University of California San Francisco; University System of Maryland; University of Maryland College Park
  摘要：In cancer research at the molecular level, it is critical to understand which somatic mutations play an important role in the initiation or progression of cancer. Recently, studying cancer somatic variants at the protein domain level is an important area for uncovering functionally related somatic mutations. The main issue is to find the protein domain hotspots which have significantly high frequency of mutations. Multiple testing procedures are commonly used to identify hotspots; however, whe...
• HIGH-RESOLUTION BAYESIAN MAPPING OF LANDSLIDE HAZARD WITH UNOBSERVED TRIGGER EVENT AI翻译
  作者：Opitz, Thomas; Bakka, Haakon; Huser, Raphael; Lombardo, Luigi
  作者单位：INRAE; University of Oslo; King Abdullah University of Science & Technology; University of Twente
  摘要：Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial hierarchical models for point patterns of landslide occurrences using different types of log-Gaussian Cox processes. Starting from a competitive bas...
• BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS AI翻译
  作者：Wang, Guoqing; Datta, Abhirup; Lindquist, Martin A.
  作者单位：Johns Hopkins University
  摘要：Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large interindividual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain sys...