An Introduction to Probability, Statistics, and Uncertainty [PDF]
, 2017 Processes that are not fully understood, and whose outcomes cannot be precisely predicted, are often called uncertain. Most of the inputs to, and processes that occur in, and outputs resulting from, water resource systems are not known with certainty.
Daniel P. Loucks, Eelco van Beek
openaire +2 more sources
Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. I. Theory [PDF]
Journal of Chemical Physics, 2020 The comparison of benchmark error sets is an essential tool for the evaluation of theories in computational chemistry. The standard ranking of methods by their Mean Unsigned Error is unsatisfactory for several reasons linked to the non-normality of the ...
Pernot, Pascal, Savin, Andreas
core +4 more sources
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems [PDF]
International Conference on Machine Learning, 2023 Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting.
Marc Finzi +4 more
semanticscholar +1 more source
Statistics-based Bayesian modeling framework for uncertainty quantification and propagation
Mechanical systems and signal processing, 2022 A new Bayesian modeling framework is proposed to account for the uncertainty in the model parameters arising from model and measurements errors, as well as experimental, operational, environmental and manufacturing variabilities.
M. Ping +4 more
semanticscholar +1 more source
Physics in Medicine and Biology, 2022 Objectives. Microdosimetry is proving to be a reliable and powerful tool to be applied in different fields such as radiobiology, radiation protection and hadron therapy. However, accepted standard protocols and codes of practice are still missing.
G. Parisi, G. Schettino, F. Romano
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On Statistics, Probability, and Entropy of Interval-Valued Datasets
International Conference on Information Processing and Management of Uncertainty, 2020 Applying interval-valued data and methods, researchers have made solid accomplishments in information processing and uncertainty management. Although interval-valued statistics and probability are available for interval-valued data, current inferential ...
Chenyi Hu, Zhihui H. Hu
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Thermodynamic Unification of Optimal Transport: Thermodynamic Uncertainty Relation, Minimum Dissipation, and Thermodynamic Speed Limits [PDF]
Physical Review X, 2022 Thermodynamics serves as a universal means for studying physical systems from an energy perspective. In recent years, with the establishment of the field of stochastic and quantum thermodynamics, the ideas of thermodynamics have been generalized to small
Tan Van Vu, Keiji Saito
semanticscholar +1 more source
Predictive uncertainty assessment in flood forecasting using quantile regression
H2Open Journal, 2023 Floods and their associated impacts are topics of concern in land development planning and management, which call for efficient flood forecasting and warning systems.
Amina M. K., Chithra N. R
doaj +1 more source
Brazilian network of PhDs working with probability and statistics [PDF]
Brazilian Journal of Probability and Statistics, 2017 Statistical and probabilistic reasoning enlightens our judgments about uncertainty and the chance or beliefs on the occurrence of random events in everyday life. Therefore, there are scientists working with Probability and Statistics in various fields of
L. Digiampietri +4 more
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