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  1. Abstract We present a hierarchical Dirichlet regression model with Gaussian process priors that enables accurate and well-calibrated forecasts for U.S. Senate elections at varying time horizons. This Bayesian model provides a balance between predictions based on time-dependent opinion polls and those made based on fundamentals. It also provides uncertainty estimates that arise naturally from historical data on elections and polls. Experiments show that our model is highly accurate and has a well calibrated coverage rate for vote share predictions at various forecasting horizons. We validate the model with a retrospective forecast of the 2018 cycle as well as a true out-of-sample forecast for 2020. We show that our approach achieves state-of-the art accuracy and coverage despite relying on few covariates.
    Free, publicly-accessible full text available January 1, 2024
  2. Free, publicly-accessible full text available January 1, 2024
  3. Free, publicly-accessible full text available October 1, 2023
  4. ABSTRACT We present a new catalogue of Damped Lyman-α absorbers from SDSS DR16Q, as well as new estimates of their statistical properties. Our estimates are computed with the Gaussian process models presented in Garnett et al., Ho, Bird & Garnett with an improved model for marginalizing uncertainty in the mean optical depth of each quasar. We compute the column density distribution function (CDDF) at 2 < z < 5, the line density (dN/dX), and the neutral hydrogen density (ΩDLA). Our Gaussian process model provides a posterior probability distribution of the number of DLAs per spectrum, thus allowing unbiased probabilistic predictions of the statistics of DLA populations even with the noisiest data. We measure a non-zero column density distribution function for $N_{\rm {HI}}\lt 3 \times 10^{22} \, \rm {cm}^{-2}$ with $95{{\ \rm per\ cent}}$ confidence limits, and $N_{\rm {HI}}\lesssim 10^{22} \, \rm {cm}^{-2}$ for spectra with signal-to-noise ratios >4. Our results for DLA line density and total hydrogen density are consistent with previous measurements. Despite a small bias due to the poorly measured blue edges of the spectra, we demonstrate that our new model can measure the DLA population statistics when the DLA is in the Lyman-β forest region. We verify ourmore »results are not sensitive to the signal-to-noise ratios and redshifts of the background quasars although a residual correlation remains for detections from zQSO < 2.5, indicating some residual systematics when applying our models on very short spectra, where the SDSS spectral observing window only covers part of the Lyman-α forest.« less
  5. Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has assumed access to a faithful (and expensive) oracle reporting experimental results. However, some settings offer access to cheaper surrogates such as computational simulation that may aid in the search. We propose a model of multifidelity active search, as well as a novel, computationally efficient policy for this setting that is motivated by state-of-the-art classical policies. Our policy is nonmyopic and budget aware, allowing for a dynamic tradeoff between exploration and exploitation. We evaluate the performance of our solution on real-world datasets and demonstrate significantly better performance than natural benchmarks.
  6. ABSTRACT We develop an automated technique to measure quasar redshifts in the Baryon Oscillation Spectroscopic Survey of the Sloan Digital Sky Survey (SDSS). Our technique is an extension of an earlier Gaussian process method for detecting damped Lyman α absorbers (DLAs) in quasar spectra with known redshifts. We apply this technique to a subsample of SDSS DR12 with BAL quasars removed and redshift larger than 2.15. We show that we are broadly competitive to existing quasar redshift estimators, disagreeing with the PCA redshift by more than 0.5 in only $0.38{{\ \rm per\ cent}}$ of spectra. Our method produces a probabilistic density function for the quasar redshift, allowing quasar redshift uncertainty to be propagated to downstream users. We apply this method to detecting DLAs, accounting in a Bayesian fashion for redshift uncertainty. Compared to our earlier method with a known quasar redshift, we have a moderate decrease in our ability to detect DLAs, predominantly in the noisiest spectra. The area under curve drops from 0.96 to 0.91. Our code is publicly available.