Forecasting in dynamic factor models using Bayesian model averaging

This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. For both GDP and inflation, we find that the models which contain factors do out-forecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable

Bayesian epidemic models for spatially aggregated count data

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time‐varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio‐temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time‐varying covariates through an Ornstein–Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g‐priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process‐based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy‐focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece

Methods and tools for Bayesian variable selection and model averaging in normal linear regression

In this paper, we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available R-packages, summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed (can) lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages

Convective storms in closed cyclones in Jupiter's South Temperate Belt: (I) observations

On May 31, 2020 a short-lived convective storm appeared in one of the small cyclones of Jupiter's South Temperate Belt (STB) at planetographic latitude 30.8S. The outbreak was captured by amateur astronomer Clyde Foster in methane-band images, became widely known as Clyde's Spot, and was imaged at very high resolution by the Junocam instrument on board the Juno mission 2.5 days later. Junocam images showed a white two-lobed cyclonic system with high clouds observed in the methane-band at 890 nm. The storm evolved over a few days to become a dark feature that showed turbulence for months, presented oscillations in its drift rate, and slowly expanded, first into a Folded Filamentary Region (FFR), and later into a turbulent segment of the STB over a timescale of one year. On August 7, 2021, a new storm strikingly similar to Clyde's Spot erupted in a cyclone of the STB. The new storm exhibited first a similar transformation into a turbulent dark feature, and later transformed into a dark cyclone fully formed by January 2022. We compare the evolution into a FFR of Clyde's Spot with the formation of a FFR observed by Voyager 2 in 1979 in the South South Temperate Belt (SSTB) after a convective outburst in a cyclone that also developed a two-lobed shape. We also discuss the contemporaneous evolution of an additional cyclone of the STB, which was similar to the one were Clyde's Spot developed. This cyclone did not exhibit visible internal convective activity, and transformed from pale white in 2019, with low contrast with the environment, to dark red in 2020, and thus, was very similar to the outcome of the second storm. This cyclone became bright again in 2021 after interacting with Oval BA. We present observations of these phenomena obtained by amateur astronomers, ground-based telescopes, Hubble Space Telescope and Junocam. This study reveals that short-lived small storms that are active for only a few days can produce complex longterm changes that extend over much larger areas than those initially covered by the storms. In a second paper [In tilde urrigarro et al., 2022] we use the EPIC numerical model to simulate these storms and study moist convection in closed cyclones.We are very thankful to the large community of amateur observers operating small telescopes that submit their Jupiter observations to databases such as PVOL and ALPO-Japan. We are also grateful to two anonymous reviewers for their comments that improved the clarity of this paper. This work has been supported by Grant PID2019-109467GB-I00 funded by MCIN/AEI/10.13039/501100011033/and by Grupos Gobierno Vasco IT1366-19. PI acknowledges a PhD scholarship from Gobierno Vasco. GSO and TM were supported by NASA with funds distributed to the Jet Propulsion Laboratory, California Institute of Technology under contract 80NM0018D0004. C. J. Hansen was sup-ported by funds from NASA, USA to the Juno mission via the Planetary Science Institute. IOE was supported by a contract funded by Europlanet 2024 RI to navigate Junocam images, now available as maps in PVOL at http://pvol2.ehu.eus. Europlanet 2024 RI has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 871149. G.S. Orton, S. R. Brueshaber, T. W. Momary, K. H. Baines and E. K. Dahl were visiting Astronomers at the Infrared Telescope Facility, which is operated by the University of Hawaii under contract 80HQTR19D0030 with the National Aeronautics and Space Administration. In addition, support from NASA Juno Participating Scientist award 80NSSC19K1265 was provided to M.H. Wong. This work has used data acquired from the NASA/ESA Hubble Space Telescope (HST) , which is operated by the Association of 807 Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These HST observations are associated with several HST observing programs: GO/DD 14661 (PI: M.H. Wong) , GO/DD 15665 (PI: I. de Pater) , GO/DD 15159 (PI: M. H. Wong) , GO/DD 15502 (PI: A. Simon) , GO/DD 14661 (PI: M. H. Wong) , GO/DD 16074 (PI: M.H. Wong) , GO/DD 16053 (PI: I. de Pater) , GO/DD 15929 (PI: A. Simon) , GO/DD 16269 (PI: A. Simon) . PlanetCam observations were collected at the Centro Astronomico Hispanico en Andalucia (CAHA) , operated jointly by the Instituto de Astrofisica de Andalucia (CSIC) and the Andalusian Universities (Junta de Andalucia) . This work was enabled by the location of the IRTF and Gemini North telescopes within the Mauakea Science Reserve, adjacent to the summit of Maunakea. We are grateful for the privilege of observing Kaawela (Jupiter) from a place that is unique in both its astronomical quality and its cultural signifi-cance. This research has made use of the USGS Integrated Software for Imagers and Spectrometers (ISIS) . Voyager 2 images were accessed through The PDS Ring-Moon Systems Nodes OPUS search service

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine