Hasil untuk "United States"

C. Johannes, T. Le, Xiaolei Zhou et al.

M. Hurd, P. Martorell, K. Langa

G. Nowacki, M. Abrams

U. Soytaş, Ramazan Sari, B. Ewing

Valerie J. Rock, A. Malarcher, Jennifer W. Kahende et al.

J. Birkmeyer, T. Stukel, A. Siewers et al.

T. Karl, R. Knight

F. Curriero, Karlyn S. Heiner, J. Samet et al.

S. Kurtz, F. Mowat, K. Ong et al.

M. Edmond, Sarah E. Wallace, D. McClish et al.

Josefa M. Rangel, P. Sparling, C. Crowe et al.

Surveillance data from 350 U.S. outbreaks of Escherichia coli O157:H7 are analyzed.

B. Grant, D. Hasin, S. Chou et al.

S. Barnes

D. Eaton, L. Kann, S. Kinchen et al.

Solveig A Cunningham, Michael R. Kramer, K. Narayan

J. Lawler, D. Lewis, E. Nelson et al.

Oa Us Epa

V. Wallis

Song Yang

The Southeast Asian monsoon is characterized by many features that are distinct from those of the East Asian monsoon, including monsoon intensity and evolution. They are also influenced differently by external factors and affect global climate in diverse ways. Studies that consider these factors should yield a better understanding of both monsoon components.

Biprateep Dey, David Zhao, Brett H Andrews et al.

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all x , and when needed, to reshape the densities of y toward ‘instance-wise’ calibration. This paper introduces the local amortized diagnostics and reshaping of conditional densities (LADaR) framework and proposes a new computationally efficient algorithm ( Cal-PIT ) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs). Cal-PIT learns a single interpretable local probability–probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, where Cal-PIT achieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyzes ^9 .